Improving audit risk assessments with AI-driven analysis of Accounts Receivable and Accounts Payable subledger data

Improving audit risk assessment longterm

The cornerstone of well-planned and high-quality audit engagements is a robust risk assessment process. Such a process is critical to identifying risks of material misstatement and their relative significance by providing a fulsome understanding of the entity subject to audit and the environment in which it operates.

The nature and extent of these audit risk assessment procedures will certainly differ from engagement to engagement, reflecting different types of operations, industries, and financial reporting complexities, however preliminary analytical review procedures are a common thread across all audits as a requisite component of the risk assessment process.

Traditional preliminary analytical review procedures

Practically speaking, preliminary analytical review procedures could include any combination of the following (not exhaustive):

  • Comparing actual financial performance to historical trends and balances
  • Reviewing actual financial performance (ratios, key financial metrics) against industry benchmarks
  • Reviewing actual financial performance compared to management forecasts and/or budgets
  • Performing inquiry of management to ascertain operational drivers for certain trends and patterns in the year-over-year results (i.e., “what’s changed?”)
  • Examining any material new contractual agreements executed in the period (leases, customer contracts, debt agreements, etc.)

Traditionally, these types of analytical review procedures take place at the level of how the financial statements aggregate the data by account or class of transactions, or perhaps at more granular levels of the chart of accounts. For example, you may compare how gross margin in the current period compares to historical periods or how increases in inventory year-over-year tracks with corresponding movement in the cost of sales accounts. In any case, it is ultimately the general ledger trial balance data and activity detail that underpins this type of review.

With a view towards a robust risk assessment process and obtaining a deep and operationally relevant understanding of your client’s business environment and financial performance, analysis and interrogation of the AR and AP subledger data as a complement to the traditional preliminary analytical review procedures at the financial statement level could be a source of highly valuable context to the results and empower you to conduct a more focused inquiry of your client’s management.

Accounts Receivable & Accounts Payable as critical inputs to audit risk assessment

Visualizing and interrogating subledger data can provide high-value insights and expose “root causes” behind some of the general ledger variances and patterns identified as part of your traditional preliminary analytical review procedures. This empowers you to better pinpoint an assessed risk and tailor your testing approach to most efficiently respond to that assessed risk.

Some examples of how to best leverage subledger information include:

Understanding how certain vendor and customer aged balances trend throughout the year

The aggregate total values of AR and AP at balance sheet dates might be relatively consistent year-over-year but there may be cause for further investigation and inquiry if, for example, the monthly ending balances demonstrate significant volatility throughout the year or seem out of pace with corresponding monthly sales or purchasing trends.

Understanding operational key performance indicators for customer and vendor “health”, and tracking those over the audit period

Tracking basic operational metrics like Days Outstanding and Turnover ratios, for specific vendors, customers, and in total, provides a lens of relative customer “quality” or vendor settlement patterns that may allow for risk to be identified more granularly. Comparing these ratios for a particular customer against the “aggregate” value allows you to identify specific customers or vendors that lag the overall average and therefore may indicate an existence or valuation risk around those balances or underlying contracts.

Expose the nature and volume of transactions on credit with related-party customers and vendors

Reviewing the subledger detail for transactions with all related entities is information that may not be readily available on the surface of the general ledger data and the relative dollar volume and activity of these transactions could be relevant to how risk is assessed around the accuracy, valuation, and presentation assertions.

Surface invoices or other records in the subledger (debit or credit memos, unapplied payments, etc.) that may be significantly aged

Isolating items in the subledgers that are significantly aged may tie directly to the risk around valuation and existence of these items specifically. Under a more nuanced lens, the existence of these types of stale records (or lack thereof) may be a relevant consideration to corroborating your understanding of the controls framework and how closely the subledgers are being reconciled and actively maintained.

Evaluate the volume and frequency of transactions at the level of a specific customer or vendor to corroborate inquiry of management and your understanding of the entity

Understanding basic data points around volume and frequency of transactions with a particular customer or vendor may help corroborate information learned from inquiry or your knowledge. For example, reviewing transactions with the entity’s landlord to confirm that 12 monthly equal rent payments were posted. Scanning this type of activity (either manually or with automated techniques) can surface invoices or payments for amounts that are potentially unusual for a certain customer or vendor and therefore perhaps may be indicative of risk.

Review for the volume and frequency of manual adjustments directly to the subledger detail

Manual adjustments or entries directly to the subledger, i.e., entries that don’t have a commercial document of record (invoice, cheque, credit memo, etc.) associated to them, may indicate fact patterns or internal processes that warrant further consideration from an audit perspective.

Perform basic statistical and rules-based tests and interrogate the subledger data to inform risk assessment

Certain procedures around data quality that are traditionally associated with journal entry testing, such as the following, may be very relevant to the subledger information. This includes any “hits” that would be relevant to deepen your understanding of your client’s accounting system and internal control framework and also advise the severity of assessed risk:

  • Reviewing descriptions for suspicious keywords
  • Duplicate document IDs
  • Two-digit Benford analysis
  • Other rules-based tests

How MindBridge automates and streamlines AR & AP subledger analysis

MindBridge AI has dedicated AR and AP modules that automatically analyze the subledger data and, without any scripting, provide high-value visualizations of the data and transaction-level analysis. These capabilities empower you to leverage subledger-level insights and anomalies as critical inputs to the audit risk assessment process.

Trends and patterns

Ai Auditor provides the ability to visualize how monthly AR and AP balances or net monthly activity tracks over multiple years, at the customer and vendor level and also in aggregate. The visualization is customizable and provides the ability to compare certain customer or vendor trend lines against each other and identify patterns of deviation.

Vendors and customers who are related parties to the entity subject to audit are flagged directly in the summary detail to identify for specific review. 

internal audit tools

Key performance indicators

Days Outstanding and Turnover ratios are calculated at the customer and vendor level and visualized on a monthly basis, allowing you to identify where there are periods of potential distress or deteriorating quality. Similar to the ending balances and activity, you are also able to customize the visualization and compare certain customers or vendors against each other along the lines of these metrics to expose patterns of interest.

internal audit results

Ai Auditor also automatically identifies any new customers or vendors in the audit period, allowing you to identify the related volume of sales or purchasing growth specific to these entities.

Aging

Aging at the customer and vendor level is automatically calculated and captured across respective buckets of days outstanding (0-30 days, 31-60 days, etc.). For certain entries that are significantly aged or stale, you’re able to drill-in to all the transactions with a particular customer or vendor and ascertain which invoice(s) are contributing to those totals.

internal audit and governance

Data interrogation and risk

Navigating and querying the transactional level data via the Data Table in Ai Auditor provides a powerful and effective way to explore and validate the subledger activity. The Filter Builder functionality allows for multiple conditions to be placed on a query, using any element of the transactional record (date, amount, user, entry type, etc.). This allows you to build and save functions that allow you to get a sense of the type, frequency, and volume of transactions with certain vendors or customers.

reasonable assurance audit

Control Points, which are various statistical, rules-based, and machine learning tests, are run against every transaction and the results are summarized on a dashboard that supports interactions like filtering and drill-through.

corporate internal auditor

Combining the query building capabilities of the Data Table with the feature of every transaction being scored against the various Control Point tests, you are empowered to identify relevant populations for sampling and have selections automatically identified on a risk-stratified basis. Approaching the sampling process through the lens of transactional risk ensures that you’re focusing your audit procedures around the entries which appear anomalous.

Take the first step towards unlocking critical subledger-level insights for risk assessment

To learn more about Ai Auditor and subledger analyses, contact sales@mindbridge.ai.

AI in finance: Helping professionals shift from hindsight to insight to foresight

Stopping dominoes with foresight

We are facing an unprecedented time of global uncertainty created by the COVID-19 virus that has unleashed a global healthcare crisis. Humanity is fighting a war against an invisible enemy that is attacking humans around the world and sparing no country. We need not be pessimistic or optimistic but rather realists and learn from the history of humanity. Human ingenuity will prevail, and humanity will survive.

We have entered a new world after COVID-19 with very different assumptions than we had in the old world when the world GDP yielded a record high of $85T. The world GDP has been severely impacted by the lockdown stipulations that were imposed to minimize the spread of the virus within the population. The key pillars of the economy are consumer and companies’ spending. If this slows down, it can lead to a recession and even depression. The lockdown restrictions are being relaxed and governments and central banks around the world are injecting massive amounts of funds into the hands of individuals and companies in an effort to reopen the economy to avoid an economic crisis.

How can artificial intelligence in finance help organizations pull through?

A renewed focus on financial errors

During economic uncertainty, an added vigilance is needed by those responsible to ensure the accuracy and integrity of the financial records that are being relied upon to make decisions about the operations of their organizations. A report by the Association of Certified Fraud Examiners (ACFE) “2020-Report to the Nations”- 2020 Global Study on Occupational Fraud and Abuse estimates that the yearly cost to the world due to fraud and abuse is about $4.5T or 5% of the world GDP. They examined over 2500 cases from 125 countries with combined losses of $3.6B with an average loss by case of $1.5M and a typical case lasting 14 months before being detected.

Whereas corruption was the most common type of fraud, the most costly were financial statements fraud schemes, even though they represented only 10% of the cases.  The breakdown of the detection methods reveals that analytics plays only a small role in the detection of occupational fraud: Human tips; 43%, internal audit; 12%, management reviews; 5%, by accident; 5%, whereas external audit catches only 4%.

A 2019 survey by Blackline provided insights into the concerns by executives with inaccuracies in financial data. With over 1100 C-suite executives and finance professionals from mid- to large-size organizations around the world, the white paper stated that:

“55% are not confident that they can identify financial errors before reporting results, 70% claim that their organizations made a significant business decision based on inaccurate financial data and 26% are concerned over errors that they know must exist but they have no visibility”.

 

The power of AI in finance

Finance professionals that rely on outdated tools and methodologies do not offer the best visibility into finding errors, errors with intent, errors that are considered fraud, and general mismanagement of the financial dataset in their organizations. The world is already witnessing a major trend toward moving to the cloud and becoming digital native and these must be vigorously pursued by organizations that want to be of the forefront of growth post the crisis.

Becoming digital native enables companies to move towards a near real-time view of their financial data and, coupled with AI in finance functions, the ability to fully analyze 100% of transactions. This ensures transparency to key stakeholders such as board members and auditors and aids in the identification of any anomalies in their financial records.

Currently, a company’s financial records are examined by external auditors on a yearly basis and evaluated using a sampling method that leaves the bulk of the dataset untouched. This method of rear view-mirror assessment provides C-suite executives with a hindsight perspective and the fear that decisions are made based on inaccurate and untimely information. Using AI-based tools to review 100% of the financial records in near real-time offers C-level executives with insights into data and, by using the appropriate analytics built into the AI applications, offers foresights into the operations of the company.

The two most important behaviors that companies must have to thrive post COVID-19 are resilience and adaptability. Resilience is defined as the ability to withstand or recover quickly from difficult conditions whereas adaptability is defined as the quality of being able to adjust to new conditions. Companies must build their operations and culture around resilience and adaptability so they can work efficiently during the “new normal” when we emerge out of this dark tunnel will become stronger and better off.

An article published by the Boston Consulting Group titled “The Rise of the AI-Powered Company in the Postcrisis World” highlights the tremendous opportunity for companies that are going to digital native, moving to the cloud, and adopting AI in finance applications to supercharge their operations. Arvind Krishna, in his inaugural speech as IBM’s new CEO, said, “I am predicting today that every company will become an AI company – not because they can, but because they must. Digital transformation means putting artificial intelligence at the center of workflows, and using the insights generated from that process to constantly improve products and services.”

 

Audit in the age of COVID-19: A guide

a complex amount of data put into a pattern

In today’s world of COVID-19, accounting firms can be pressed by clients for short-term basic deliverables. There are still expectations for getting audits completed as pragmatically as possible per the standards along with the new norm of engaging clients virtually and securely.

I believe this challenge presents an opportunity for firms to re-position themselves for success in addressing this new environment. Humans adjust to new challenges well and this will accelerate the digitization and automation of key accounting processes, including auditing. This automation will not happen overnight but will transition as a step change, where the audit of the future bears little resemblance to the audit of today.

“We believe that the application of artificial intelligence will be immensely valuable in helping companies adapt to these trends. The most successful use cases will be those that seamlessly combine AI with human judgment and experience.”

– The Rise of the AI-Powered Company in the Postcrisis World, Boston Consulting Group

The accounting firms that adjust with speed and agility will be well-positioned to grow today and in the future.

Audit analytics as a competitive differentiator

The topical issues we are witnessing today include ‘going concern’ and financial performance analysis. Tremendous focus is being applied to cash flow, debt management, and yes, business survival. Businesses that are feeling financial stress are seeking additional education and guidance.

Remember, many times clients do not know what they want or what you can do for them. This is a perfect time to make your firm relevant to your clients. You can critically evaluate what is good for your clients and do so by leveraging audit analytics tools to educate them and develop appropriate plans to address their challenges.

type of audit sampling

Audit analytics builds usable market or industry-specific knowledge, so while you might not be able to sell advisory services directly to your audit clients, having better market knowledge improves your ability to sell this value to other companies and win new customers. Your firm becomes more relevant to their business and you have created a competitive advantage for your firm.

Remote auditing begins with security

It will also be paramount to ensure that the technology and services you leverage are designed to work in a virtual environment securely. Ensure that your audit tools are cloud-based, have primary and backup infrastructure providers that are fully ISO 27001 and SSAE 16 compliant, and use data encryption using NIST-approved algorithms (AES 256). It’s also critical that the vendor has completed the AICPA Security Organization Controls (SOC) compliance and has completed its SOC 2 Type 2 certification against all five trust services criteria.

Audit analytics in action

A MindBridge customer spoke recently about the value of our platform and how it positions the CPA firm for success. They stated that Ai Auditor improves the client-auditor relationship by allowing them to ask better questions and enhance the knowledge of their client business more than traditional tools. The platform becomes the single source of information that leverages the general ledger and provides 100% coverage with its transactional approach.

This positions accounting firms to:

  • Understand and guide their clients appropriately, especially in the storm we are witnessing today
  • Critically evaluate what’s good for their clients, including risk awareness and management
  • Educate clients on their options and provide appropriate advice in making better decisions for their business

Accounting firms can re-invent and position themselves for success in this new world by delivering value-added services primarily driven by what is good for their clients. Your commitment to deploying innovative new solutions, such as audit analytics, and creating a competitive advantage for your firm is critical in today’s world of a virtual practice. Internally, firms and their employees will be inspired with the latest innovations and automations that our platform delivers.

There are risks in making this transition and investment but by working with the right partners and being astute about it, the benefits for your practice will be considerable.

Quality audit vs. efficiency: How technology bridges the gap

audit sampling techniques

As someone who has worked in big-firm transformational change for quite a while, I’ve often reflected on the perils of success and its ability to stand in the way of innovation and agility. We work really hard in transformation teams to articulate URGENCY, and NECESSITY, and ABSOLUTE DIRE NEED to transform. But no matter how creative we are, without a great deal of immediate pain, it is incredibly difficult to steer a new course for big ships that have been historically successful.

Undeniably, COVID-19 is a catalyst for urgent and mission-critical pivoting across most areas of professional services, and indeed business in general.

I’ve been checking in with many of my clients and industry colleagues over the last couple of weeks, seeing how I can help them, seeing if they’re ok. This has helped me learn a lot about how many audit firms across Australia and New Zealand are coping.

CLOSING
DECLINING
PROVIDING
THRIVING
CURRENT STATE
Hibernating or liquidating
Open (just), panic over decline in recurring revenue
Open and BAU (as far as the market is aware)
Open & thriving
REVENUE MIX
None
Traditional recurring engagements only
Mostly traditional recurring engagements
Maintained traditional recurring engagements, adding parallel services and /or opportunistic blue-sky work
CLIENT APPROACH
In the wind
Overwhelmed by client emotional burden
Reactive client emotional support
Proactive, deliberate and direct client management
WORKFLOW FOCUS
Nil
All client work reducing
Laser focus on core client deliverables
BAU on core + new (relevant) service offerings
STRATEGIC FOCUS
Nil
Holding breath, can’t see past next week
Wishing for the dust to settle
Planning for acceleration when the dust settles

Regardless of which state you and your firm fall into, no one will argue that ‘business as usual’ is no longer a thing. All those posters we’ve all seen about needing to embrace change seem to have come rushing at us all with force.

The push-pull between audit quality and efficiency

ai in internal audit

How are firms around the country reconciling the absolute mission-critical need to be agile, to adapt, to change rapidly while protecting their sole reason for existing: Audit quality?

Materiality

Many firms are considering their materiality levels. Are those that have always been used still appropriate in the current climate? If lowering them is most appropriate to capture risk properly, how will firms deal with the significant uptick in sampling effort required as a result?

Focus on fraud

artificial intelligence audit

Pressure – Employees within our client’s businesses will be under enormous stress, thinking about potential job loss, financial strain, and social distancing.

Opportunity – Management review may not be as vigilant due to distractions by the crisis and other management responsibilities, and internal controls may be circumvented in times of crisis for the reason of expediency to keep business processes operating.

So firms are considering how they can appropriately resource, looking more closely in areas that are ripe for fraud in these economic conditions – areas like payroll, termination payments, shrinkage of inventory, revenue recognition…anywhere where there’s not great segregation of duties, management override of controls, etc.

Big picture mindset

Following a normal process, one designed for ‘business as usual times,’ is just not sufficient in the current state of COVID-19, so firms need their auditors to step back and look at more of the big picture. Considering the broader context of what they’re doing, evaluating the effects of COVID-19 and surrounding behaviours on their risk assessments is critical for effective audits.  This requires perhaps more space and ‘thinking time’ than the normal production line of audit engagements so again, firms are contemplating the effect of this change on resources and profitability of current engagements.

Carrying on as normal is unacceptable. What was identified as the top risk when you did your audit planning and risk assessment­ —what you are auditing as you read this —is almost certainly not the top risk today.

Each of these considerations means a diversion from very structured and process-driven methodologies and practices that have been refined over the recent years to deal, in many cases, with the downwards fee pressure on audit engagements. And pivoting away from ingrained processes risks the need for more WIP and more resources.

As Tim Kendall, BDO National Leader for A&A shared with the AFR recently: These things coupled with any rigidity in ASIC’s views around current financial reporting time deadlines pose a significant risk to audit quality as firms are forced to squeeze engagements into a very tight delivery timeframe. (full AFR article HERE)

The key difference between those firms who are Closing, Declining, or Providing and those firms who are Thriving is simple – either an ongoing commitment to best-of-breed tech to support the most efficient and effective high quality audit engagement delivery OR an immediate prioritising of adoption of such. Or both.

Thriving firms already have, or are seeking to have, tech that provides ways to be more efficient AT THE SAME TIME as expanding the lens to have a broader contextual view of risk.

Read more

How auditors use AI-driven ratios to understand risk

Effective audit execution in remote work environments

Getting ready for AI-powered audit in 2020

Our tips for artificial intelligence in accounting

assurance audits

A common thread among the advice given by Accounting Today’s Top 100 Most Influential people was exemplified by Alan Anderson of Accountability Plus:

“Become technology sponges. Embrace technology and explore avenues that can positively impact what accountants do on a daily basis to help their clients.”

With our unique experience in delivering AI-enabled auditing solutions to accountants around the world, we asked our experts what advice they would give to firms thinking about, or already engaged in, their journey towards artificial intelligence.

Solon Angel, Founder & Accounting Today Top 100 Most Influential People

“Technologies that bring new efficiencies are the enablers of innovative process designs. It’s easy to get carried away by the hype in new technology cycles but keeping an eye on the fundamentals and measuring them against their utilities enables you to swiftly create value for all stakeholders.”

 

Scott Rockefeller, Director, Sales

“With artificial intelligence, incremental improvement is much better than postponed perfection. Waiting to see how the market evolves means you’re much further behind whenever your arbitrary inflection point comes up. The prudent strategy is to test the waters now and to get ahead of the competition.”

Azalia Shamsaei, Product Manager

“Artificial Intelligence is not hype, it’s real, and it’s here! AI has huge potential to transform our lives, and it’s already impacting industries worldwide. Audit must embrace technology and change – as auditors of the future, you should leverage tools that incorporate new technologies and explore new ways to audit beyond today’s and yesterday’s ideas.

There is no point in delaying the change, it’s just a matter of time to adapt and know more about it. Being an early adopter not only gives you a competitive edge in hiring the right people, it also provides your clients with more effective and efficient audits that exceeds their expectations.”

 

John Colthart, Senior VP, Sales

“Since the beginning, MindBridge has worked to help our clients change their business models, recruit new talent, and most importantly, enhance their existing processes. You can’t wait for standard setters, regulations, and “sign off.”

Technology is a fundamental part of our social and business fabrics and its use allows you to grow, retain, and encourage evolution in the profession. As leaders and individuals, you can be part of the change by moving into the 21st century to be technologically enabled.”

 

James Moffatt, CPA, Director of Sales

“Everyone finds themselves so busy with client commitments that they forget to plan for next year. And the year after that. Taking the time to make time to evolve your audit practice with enabling technology means you’re better prepared for the future.”

 

Gillian Fischer, Global Manager, Customer Advocacy

“With the flurry of new opportunities around us, the pace of change can feel overwhelming. The truth is, not everything is changing. It’s your core values that stay constant. Being a trusted advisor, remaining relevant, committing to your clients, empowering your people, and maintaining integrity — these are things we typically cannot compromise.

Return to your core values for the courage to make a change in your organization and have the confidence in knowing that where you’re going is part of the story of who you are.”

 

Gordon Roxon, Account Executive

“Overnight success takes time. Get on with it!”

Kevin Smiley, Account Executive

“AI is a blue ocean opportunity, providing firms the unique ability to redefine their market boundaries and make their competition irrelevant. CPAs went through digitization using tools to automate and improve their existing way of working without really altering it fundamentally.

CPAs now are going through digital transformation by moving from one way of working to entirely new ones, capturing far more value than was possible using low-scale, low-leverage legacy business.

Firm leaders need to ask themselves: Do I want to invest in AI to create demand for my firm now or wait until I’m  compelled to in the fight for my firm’s life for market share?”

 

To learn about MindBridge Ai Auditor in 10 minutes, watch this video.

Democratizing financial and audit analytics with AI

auditing profession

PwC recently shared that in 2018 alone, 12 zettabytes of financial services industry (FSI) information was generated, but less than 0.5% was actually leveraged by businesses. This financial data explosion comes from an ever-increasing number of ERP and CRM systems employed by businesses and their partners, gathering and consolidating different payment, expense, inventory, and maintenance cost ledgers across the organizational landscape.

Contributing to this low rate of analytic engagement is the fact that current methods for analyzing financial data are slow; limited by time, capabilities and skill sets of the people and the software available to support the auditors and financial analysts.

Current analytics tools can’t keep pace

Let me illustrate this problem with a reasonably routine example. An internal auditor is asked to perform a risk analysis on a general ledger with five million rows. The Microsoft Excel limit for analysis is one million rows, preventing the auditor from using what is considered the world’s most popular analytical software. This means the auditor must engage their internal data analytics team, made up of specialized resources trained in the use of one the popular Computer Assisted Audit Tools (CAATs). This team in turn will write a script to perform the required analysis of the entire general ledger, or to save time, sample the data and attempt to extrapolate the risk.

The problem is, once the script work is scheduled and completed, the analysis might be too late for the audit process, or the priorities of the organization may have shifted to the point where the analysis is moot. If the team decided to use a sample and extrapolate the risk, they may have significantly less than a 100% risk analysis, putting the organization at risk.

It’s therefore no wonder that businesses are not making use of analytics; a world running on machine generated data, human-dependent analysis cannot keep pace.

AI enables analytics for all

This puts artificial intelligence (AI) front and center as the means of breaking this dependency on specialized resources and human-speed analysis. MindBridge is focused on developing the MindBridge Ai Auditor for auditors and finance managers to load financial data and analyze it on their own.

This allows auditors to bypass these specialized data science resources and tap directly into the power of their own data, effectively democratizing the access to financial analytics.

Microsoft CEO Satya Nadella shares that,

The core currency of any business going forward will be the ability to convert their data into AI that drives competitive advantage.

MindBridge is in complete agreement with Satya’s view, and I look forward to sharing more with you on this.

The human-AI relationship for CPAs: More, better, and faster

sample size in auditing

Every week I interview entrepreneurs and experts from around the world to share their big idea about new forms of value creation and the potential we can unlock when technology augments the unique strengths of people to deliver remarkable impact.

Transforming financial auditing

I got inspired by the big idea behind MindBridge Ai, hence I invited CTO, Robin Grosset to my podcast. We explored the challenges in the financial auditing practice, and how, even after decades of automation, much of the practice is still very manual and sample based, leaving huge opportunities for fraud. Beyond that, we discussed why a human/machine approach will always provide the optimal combination to create exponential impact.

The thing that triggered me most from my interview with Robin

“The existing ways that we are analyzing or auditing financial transactions are inadequate with the rules based system, you’re only going to find something that you anticipate.

What’s the bigger value here?

If we only find what we anticipate, i.e., the cases that are highlighted based on the rules we have set, then what is the magnitude of what we are NOT catching? Robin addressed this by highlighting recent research from the Association of Certified Fraud Examiners. The number appears to be a number beyond imagination — but to put it in perspective — For every $1 we label as ‘fraud, misconduct, or irregularity,’ we’re missing out on $15. So, with current systems we’re only tracing 6.6%, and missing out on 93.4%! Translated, this is $500 for every person on the planet – every single year. And apparently (until recently) nobody was making a big issue about this, arguing ‘it’s not necessary to do anything different, this is the way we work’. This is a typical example of complacency and inertia in the workplace.

It’s about time the rules-based systems are going to be replaced by self-learning systems that are 24/7 active on finding new patterns, i.e., the +93% we’re missing out on. It’s the only way to win the fight against fraud. Doing nothing is not an option as data volumes and the number of channels we operate in keep increasing with extreme pace.

What significant fraud detection opportunity is raised?

What would be the impact on the economy and on society as a whole if this was solved? From my perspective, this is not only about finding the leaks in our systems, but very much about what we could do with the difference. Just look at the challenges we’re facing in health, education, or for example, public safety, simply because budgets are cut every single year. If these organizations would be able to 10x their ability to find fraud, misconduct, and irregularity — what could they do with that difference?

I would assume that there are many more areas like this to be uncovered — an opportunity and obligation for all of us to be sensitive about. I concur with Robin’s advice to look beyond the established conventions and existing standards. Only then will we be able to disrupt the status quo and increase (competitive) advantage.

On that notion — I concur 100% with Robin’s vision that the way to go about this is human-centric AI. In many industries, ‘black-box’ automation won’t work. Just think about how to explain black-box decisions in court? You’ll always need a person with a high-level understanding of the business context. Therefore, it’s about augmentation, not automation. Augmentation will allow human auditors to take their game to the next level, perform a better service to their clients, and be able to back their decisions up with clearly articulated evidence.

In other words: don’t be afraid that AI will take our jobs. It will not.

That said, doing nothing is not an option either: human auditors using AI will replace auditors who don’t. That’s an idea worth thinking about — also if you’re not an auditor.

Listen to the big idea behind MindBridge Ai, and why it has the potential to transform the way financial auditors deliver value.

Our year in stories: 2018

internal audit purpose

We’re grateful to be a part of the world’s journey towards AI. Far more than an academic abstraction, 2018 was a leap forward in the practicalities of AI and machine learning across many different applications, with our own vision for the transformation of audit and financial analysis gaining momentum across the globe.

Here are some of the best and brightest spots of our year together in AI.

“ must be developed and used while respecting people’s autonomy, and with the goal of increasing people’s control over their lives and their surroundings.” – Montreal Declaration for a Responsible Development of AI

The social and ethical challenges of AI are just beginning to be realized, and the recent signing of the Montreal Declaration for a Responsible Development of AI is a big step forward in providing the framework for responsible technology development. As the first private sector signatory to the Declaration, we reinforced our commitment to responsible, human-centric AI systems.

Through a passion for enabling technology, Samantha Bowling, CPA, CGMA, was named a 2018 Innovative Practitioner by CPA.com. As the first to successfully use AI in auditing for small businesses, non-profits, and local government, Samantha’s firm, Garbelman Winslow, leads the pack in improving processes and reducing the risk of material misstatements.

“We need to figure how to free up more data so that AI can thrive.” – Leon Katsnelson, Director & CTO, Strategic Partnerships and Data Science Ecosystem, IBM, speaking at IMPACT AI

The inaugural IMPACT AI conference was held on May 24th, bringing industry thought leaders and technology experts to an audience of over 550 people. In addition to promoting AI education, the goal of the event was to increase and elevate more women in technology. Watch Navdeep Bains, Canadian Minister of Innovation, Science and Economic Development discuss the influence of AI and stay tuned for details on next year’s conference.

Industry reform was a big theme in accounting this year, with scandals for the Big Four and the UK Competition and Markets Authority recommending major shake-ups. Our CEO, Eli Fathi, reminded us how technology can play a critical role in reform.

The first known case of AI helping to investigate a human CPA committing over $2.8M in embezzlement fraud was documented on the ACFE Insights blog.

“AI is transforming the way auditors do business and the exponential pace of change is requiring CPAs to get up to speed quickly.” – Tom Hood, CPA, President & CEO, Maryland Association of CPAs

With dozens of events, webinars, seminars, and forums under our belts in 2018, two notable ones were our AI & the Future of Accounting roadshow, in partnership with the Canadian Trade Commissioner Service, and our expert CPA panel in December. While the roadshow introduced AI to audiences across eight cities, the expert panel delivered practical advice and recommendations tailored directly for auditors. We were also recognized by industry associations and media this year, including being selected as the Top New Product of 2018 by Accounting Today and the Best Machine Learning Solution for Regulatory Compliance by Central Banking.

After a successful pilot with universities across North America, we launched our University Alliance Program in July to educate and train accounting students on the use of AI in auditing. As this year ends, the momentum will continue into 2019 with more than double the amount of institutions on board, over 1300 students completing the program, and a wealth of new curriculum materials and case studies being generated.

Our partnerships with accounting firms around the world exploded, growing our user base to well over 200 organizations. Relationships such as with Garbelman WinslowKNAV P.A., and Kreston Reeves, solidify the value that AI brings to auditing and help us continually improve the MindBridge platform.

For our development team, 2018 was a year of transition as we went from launching the first release of MindBridge Ai Auditor to continuous delivery of major new features for users. February saw new functionality such as Natural Language Processing (NLP) and accounts payable launched at a marquee event in partnership with the Canadian Trade Commissioner Service at Canada House in London, UK, while the rest of the year saw delivery of discrete pieces of value for users, such as interim audit reviews, the data ingestion wizard, and the amazing Filter Builder used by auditors to create their own AI-enabled tests and logic.

What will 2019 bring? We firmly believe that AI is still in its revolution stage for many, bringing aboard new players all the time, while others continue to work with AI-based audits every day. We’ll continue to share and educate along the way, and hope that you’ll let us know how we’re doing.

Why we signed the Montreal Declaration for a Responsible Development of Artificial Intelligence

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With artificial intelligence (AI) influencing every aspect of our lives, and its continued growth in research and commercialization opportunities, the question isn’t whether we should develop it responsibly, it’s a question of how.Last year, over 400 participants came together at the Forum on the Socially Responsible Development of Artificial Intelligence to discuss themes of cybersecurity, legal liability, moral psychology, jobs, and other areas to begin the conversation around the impact of artificial intelligence systems (AIS) on humans. Given that it’s now possible to create autonomous systems capable of performing tasks that were once the sole domain of human intelligence and have strong influence on data-driven decisions, it’s imperative to consider the potential effects of AI on ethical and social concerns. How will AI impact security and privacy? What is the impact on social equality and cultural diversity? Will AI disrupt careers and upend the job market?

These are tough questions and the result of the 2017 forum was a draft declaration setting out a framework of ethical guidelines for the development of AI. After a months-long consultation process with the public, experts, and government decision makers, the final Montreal Declaration for a Responsible Development of Artificial Intelligence was signed on December 4, 2018 at the Society for Arts and Technology.

As of today, we are the first private sector signatory to the Declaration, reinforcing our commitment towards an ethical framework for AIS technology development. The Declaration has three main objectives:

  1. Develop an ethical framework for the development and deployment of AI
  2. Guide the digital transition so everyone benefits from this technological revolution
  3. Open a national and international forum for discussion to collectively achieve equitable, inclusive, and ecologically sustainable AI development

How the Montreal Declaration applies to us

As MindBridge is building an AI platform to help people analyze and understand vast amounts of their data in ways never thought of before, it’s critical to follow a development philosophy that keeps our users at the center of the loop. Because we’re building it for you.

We firmly believe that AI is not meant to replace humans, rather its greatest benefit is to empower people to make better decisions for themselves and society without imposing constraints based on any specific beliefs. As the Declaration’s “Respect for autonomy” principle guides:

AIS must be developed and used while respecting people’s autonomy, and with the goal of increasing people’s control over their lives and their surroundings.

Another principle is democratic participation, where “AIS processes that make decisions affecting a person’s life, quality of life, or reputation must be intelligible to their creators.” Our human-centric approach to the MindBridge platform embodies this philosophy within every aspect of the system. Our CTO, Robin Grosset, explains the details and provides concrete examples in his recent blog.

We embraced these and other principles long before the Declaration was signed, so it required little thought to join and become the first private company to get on board. Now that it’s official, we look forward to working with industry, government, and other parties to ensure a responsibly-developed AI future for all of us.

Our approach to human-centric artificial intelligence

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Where is the AI?

Artificial intelligence (AI) is all around us, it powers the helpful voice on my phone and it’s in the digital assistant on my kitchen counter. Actually, I have to admit liking to say “Alexa turn on Christmas” to turn my Christmas lights on and off. It’s just a simple end-point computer, like a terminal, communicating with a cloud-based service which does all the hard work of interpreting what I say and figuring out what to do.

Many AI systems are not as obvious as Alexa, they surround us, yet we don’t see them. Take the ads on my Facebook feed, for example, an algorithm is figuring out what it knows about me and then what ads will likely work best. Even with Google, what appears to be just a search box is much smarter. If you ask the question “What is the population of Canada,” Google is not just searching documents using its famous PageRank algorithm, it’s doing much more. It’s figuring out that an infographic is the best way to communicate the population of Canada to me and showing this alongside its other insights. It also knows flight numbers and does different things depending on context.

What we think is a simple search is much more. AI is sometimes quite subtle and helping us in ways we may not realize.

Good experience design often makes our little AI helpers invisible to us. Two of the ten Dieter Rams principles of good design are, “Good design is unobtrusive” and “Good design is as little design as possible.” We can see why subtle or invisible AI happens; it is considered good design.

 

Does MindBridge hide its AI?

We have a philosophy that when our AI provides insight or direction to users, we give them the feedback they need to both see it and understand it. We believe in human-centric AI, which means the human is the central part of the system and they should be able to understand what the AI is telling them and have explanations at each stage. The AI needs to communicate and therefore, being visible is an essential element in the trust relationship we are endeavoring to create.

Having said that, sometimes we can’t help ourselves and occasionally we make the experience seamless and require users to click on little information tabs to find out more. This is a design principle called ‘progressive disclosure’ and allows a user to select the level of detail they want.

So where is our AI? How do you know it’s there and working? Let’s take three examples from our AI Auditor product and walk through the techniques and the design considerations.

 

#1 Unobtrusive but verifiable

Auditors often have to classify items in audit tools manually. They may need to say what kind of money is held in a certain type of account, whether it’s a cash asset, a liability, or maybe a non-capital expense. This process of instructing a software tool in what something means is laborious and repetitive. I think it’s fair to say nobody wants to do it but it’s required to get an accurate view of the finances. This is a great candidate for automation with AI.

MindBridge has a built-in account classifier that uses the human-readable label on financial accounts to determine what kind of account it actually is. This is a form of language processing and we use two methods, the first is a simple search which works well for well-labelled accounts, the second is a Neural Network Classifier which learns how people classify accounts. The net effect (excuse the pun ☺) is that most users of MindBridge spend little to no time telling our system what an account is. It just knows. We do recommend, however, that users review its findings to confirm or correct them. Our AI also learns from these interactions.

This is what it looks like as its working: It appears to be loading data, pretty unobtrusive and just doing its thing.This is what it looks like when the user verifies the outcome. The user has the option to change the classification of the account. This is the only real clue that something smart has just happened.You could be forgiven for not noticing that a lot of work is happening but there are some real time savings here. Below are some charts of simple text search methods vs. a hybrid of text search and AI together. On simple and well-labelled accounting structures, the accuracy of a text search is indistinguishable from an AI. But as we get a little more complex, we see big wins. Further, as the complexity grows to involve a massive organization’s accounts, you see that the simple text search accuracy breaks down and doesn’t cope at all. Conversely, the AI method keeps on punching through the problem and gets it done. The time savings at the complex level is huge; we are talking hours, if not days, of human time saved in laborious activities.

#2 Search that tells you what it understands and gives you options

The MindBridge search interface is a little different than what you’re used to, as we want everything to be understandable and explicable even at the level of a search box. Have you ever typed a search into Google and not got the results you wanted? Chances are you ended up not scrolling to page 2, typed in a slightly different question, and got what you wanted by trial and error.

At MindBridge, we value the AI being visible and explaining itself so that our users can figure out what part of the question is driving the view of data. Here we see a search user interface where the user types their query. There is no AI yet.The user hits go! The AI system parses the language and uses natural language processing (NLP) techniques to unpack what is being requested. Our NLP AI understands language in general but also common accounting terminology. It highlights the important terms in the query and filters the transaction list accordingly.Note that the highlights are clickable so that a user can determine other possible paths and verify that the AI has understood the question. It also understands complex semantics like conjunctions, which are combinations of terms such as AND, OR, or NOT logical expressions. This allows more complex questions to be posed and answered.

In this way, MindBridge users can not only search vast amounts of transaction data for specific scenarios, they can do this without writing an SQL query or using similar technical languages. The AI is effectively reading back their query to them to help in the understanding of what’s driving the results and showing other possibilities. This user interface is very artful as it provides both progressive disclosure and explainable AI, all in a search box.

For transparency, MindBridge has filed a patent for methods used in this search interface. We believe in ‘AI for Good’ and human-centric AI and we use patent protection to ensure the freedom to do the work we do.

 

#3 Ensemble AI

Ensemble AI is the main event at MindBridge and it guides much of our work. We consider its primary role to be a focusing function for people and, as we specialize in finding insights and irregularities in financial data, it allows us to do this in a robust and explainable way.

So how does Ensemble AI work?

First, we need to understand that the ensemble is not just one method or algorithm but many. It’s like having a panel of experts with different types of knowledge and asking each of them what they think about a given transaction or element of data. The system then combines all the insights from the individual algorithms together.

For example, AI Auditor includes standard audit checks, so some of these “experts” are following simple audit rules while others follow advanced AI techniques and algorithms. The point of the ensemble model is that they all work together like an orchestra and, as the user is the conductor of the orchestra, they can select what’s important to them and the combination of results from the ensemble is presented in an easy to follow way.

Here’s an example of one of the detailed views of the ensemble at work (click to enlarge). You see all the little rectangles which have the larger red or green highlights, these are the individual AI capabilities in the ensemble.Let’s dig deeper into two of these capabilities.

 

Expert score

One example of an AI method we use is an ‘Expert System.’ This is a classical AI method that draws on the knowledge of real-world accounting practice to identify unusual transactions.

How do we capture real-world knowledge? We work closely with audit professionals and quiz them with surveys and specific questions about risky transactions, allowing us to construct an expert system that knows hundreds of account interactions and their associated concerns. We can run this method very quickly on large amounts of data, allowing us to scale human knowledge and highlight issues that a human user looking at a small sample could easily miss.

Rare flows

Ensemble AI can also identify unusual things using empirical methods. This leverages the science of what is usual or unusual, such as another method we use called ‘Rare flows.’ This part of Ensemble AI is a method of unsupervised learning from a family of algorithms known as outlier detection. The nice thing about unsupervised learning algorithms is they bring no bias, they simply identify what’s in the data and thus let the data speak for itself.

The purpose of this method is to uncover unusual financial activity. We apply this method to all financial activity but the specific PCAOB guidance on material misstatements says:

The auditor also should look to the requirements in paragraphs .66–.67A of AU sec. 316, Consideration of Fraud in a Financial Statement Audit, for …  significant unusual transactions.”

This algorithm finds unusual activity and highlights them and we also perform this type of analysis with several different ensemble techniques. One of the nice things about the ensemble is that you’re not relying on one method, and these techniques can look at account interactions, dollar value amounts, and other outlier metrics to bring them all together.

 

Why human-centric AI is needed in auditing

Most audit standards today, including the international standards, were the result of years of experience in previous cases of accounting irregularities. As such, they are great at identifying the problems of the past. The limitation is that the typical rules-based system approach to finding irregularities can never identify a circumstance that is not anticipated, and this is why we should apply AI methods like those described above.

A future-looking audit practice needs to adapt to new circumstances. Every industry is changing as the result of AI adoption and the idea that we can uncover new and unusual activity, and explain why it is being flagged, is a key strength of AI systems used by forward -looking audit professionals.

This is why we need AI in auditing. In the words of John Bednarek, Executive Director of Sales Operations, Marketing & Strategic Business Development at MindBridge, “Auditors using AI will replace auditors who don’t”. The simple reason for this is auditors who leverage AI will be faster and more complete in their work, providing a better service to their clients.

Ethical AI goes beyond legal AI

internal audit sampling

The recent case of the Statistics Canada project to use personal financial data from banks to study the spending habits of Canadians provides a very clear lesson in the ethics of AI. In this case, Statistics Canada has clear legal authority to request and use this data and it’s very likely that the proposed project conforms with ethical standards for AI and analytics. There is also an excellent case that this project will provide significant public benefit. However, it’s also clear that the project failed to gain a moral license from Canadians and by failing in this regard, they have put the project and perhaps their freedom to operate at risk.

Shining a light on the project

At this point, the details about the project are difficult to come by and I have not seen evidence of any public consultation or public notice of the project. This project came to light through a news story published by Global News on Oct 26, 2018. Based on the news reports and a bias towards the general good intentions of government bureaucracy, we can infer that Statistics Canada finds its current survey-based approach to collecting data on Canadian spending habits deeply inadequate. I also expect that the bureaucrats involved saw the opportunity to provide a more accurate picture of Canadian spending habits, more efficiently, and with less burden on the members of the Canadian public. After consulting with Justice, they also determined that they have the legal authority to do so and they honestly believe that Canadians by and large trust Statistics Canada with their personal data. So they made the decision to use the legislation governing Statistics Canada and request data from the banks. I also expect that bureaucrats knew that this request could be misunderstood by the public so they decided to act out of the public eye, trusting that the banks would comply without fuss. Of course, this project will benefit the banks greatly.

What possibly could go wrong?

Application to analytics and AI

I want to stress that there was no malice in the bureaucratic intentions behind this project. To the contrary, I see the motivations as things we want to encourage: innovation, efficiency, improved quality, and Canadian competitiveness. Where things may have went wrong is a long-standing bureaucratic culture of secrecy. The causes and solutions to this problem with bureaucratic culture is a topic for another day.

No doubt there will be calls for changes to the Statistics Act but I think cries for wholesale changes are misguided. Overall, the Act provides a good example of a legal framework for analytics. I’m not saying that events such as this should be ignored, rather the justice department should be tasked with reviewing the act and regulations with the goal of  improving the legislation — perhaps by making public consultation mandatory when Statistics Canada wants to collect personal data indirectly.

Legislative frameworks for analytics and AI must do a few things well:

  • They must protect privacy
  • They must ensure that the collection and use of personal data contributes to the general social welfare broadly defined
  • They must protect the ability to innovate

On this last point, legislative frameworks must be flexible and protect against egregious misuse while relying on social and market mechanisms to align activity with public expectations. Authority granted by legislation must protect against the right to innovate being blocked by a radical few. By these tests, the Statistics Act stands up well.

Having legal authority to do something is not the same as acting morally or ethically. In general, ethical use of personal data requires that the data subjects explicitly consent to the collection and use of their data. One can assume that the data subjects have given a license to the analytics organization to use their data for the intended purposes but, in practice, this is complicated and there are exceptions to this approach. One such exception is that the use serves the public good. From what I understand of the proposed use of data by Statistics Canada, this test is clearly met.

How we can do better

So what went wrong? The personal data in the possession of the banks was created as part of delivering banking services. The public expectation, perhaps naively, is that that is the only use they have consented to. The attempt by a third party to access and use this data to develop profiles of consumer spending habits goes well beyond their expectations. In this case, the legal authority to do this is irrelevant and disturbing. At the very least, a public education campaign describing why this is important to Canada and Canadians and how each individual will be protected in the process would have gone a long way to easing the public’s concern.

More fulsome consultation and offering individuals with the ability to opt out would likely have eliminated all barriers and created a positive opinion of the project. Each time an organization tries to fly under the radar when accessing large quantities of personal data, they create a risk of public backlash that will saddle the industry with stifling regulation.

The AI industry needs the right to ethically innovate and to do this, we need a regulatory environment that gives latitude to innovate. This requires the public to be confident that industry members will act ethically within the bounds of the legislation. Each time the AI industry goes against these expectations, the right to innovate is put at risk.

 

How accountancy can thrive in the age of AI

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The world is changing at a faster pace than ever, leading chief economist at the Bank of England, Andy Haldane, to state that the disruption caused by the ongoing fourth industrial revolution would be “on a much greater scale” than that experienced during the Victorian industrial revolution. Technology is evolving and infiltrating different industries each day and the era of artificial intelligence (AI) is very much upon us. But do employees risk becoming “technically unemployed” with this rise of technology? Or instead, could accountancy thrive thanks to the rise of AI?

Change is in the air

The adoption of new regulations around mandatory audit firm rotation has stimulated competition in the market and caused real drive for the accountancy industry. The most progressive firms have identified AI capabilities as an important differentiator, but still appreciate that the best practice is a collaborative approach, one that augments human and artificial intelligence.

In the same way that the human brain cannot compute hundreds of thousands of data points in a split second, a machine cannot always understand the and context of real-world accounting. In combination, an accountant fueled by AI is turbo-charged to make faster, more accurate decisions, while having more time to focus on providing guidance, value, and insights.

Enhancing the practice

Although proactive firms are deploying AI to help drive efficiency, reduce risk, and increase quality in their compliance processes, there still remains caution in some parts of the market. Implementing AI to augment and support the practitioners in the accountancy world has shown how this technology can benefit the industry, so why is there still hesitancy? It’s a caution that’s driven by myth, misunderstanding, and misconception regarding the perceived black-box nature of artificial intelligence. Each is an unnecessary barrier to the progress all companies need to make if they’re to compete in the modern marketplace.

Often the adoption of AI tools remains hamstrung by the idea that they cannot integrate with existing technology and are complex to use, and this comes down to a misunderstanding of what’s available. The most effective solutions are affordable and designed to work easily alongside people. They’re designed to demystify AI and make them intuitive to use. Moreover, as regulators take an increasingly tough stance on audit failures, AI solutions are a long-term investment that can reduce risk, increase efficiencies, and improve the quality of financial analysis.

Collaboration, not isolation

In the age of AI, each company must become a technology company in order to defend and grow their market, including the financial industry. It is no longer a question of if the role will change, but how can accountants equip themselves with the necessary skills to thrive in the changing world. It’s time to forge forward and recognize that accountancy actually benefits from the rise of artificial intelligence, unearthing more of the risk in financial data, and providing greater assurances than ever before.

AI is not something for accountancy to fear; it’s something for the industry to embrace in order to enhance auditing practice, increasing accuracy and efficiency.

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