How KNAV is Reimagining Audit Efficiency with Artificial Intelligence (AI)

auditing team

Today’s audit environment is more dynamic, complex and growing than ever before concerning financial data.  The challenges with globalization, the growing Internet, Big Data and ERP systems with multiple touch points fuels the growth of financial data and challenges the ability of professional accounting firms to assert high-levels of confidence in performing audits.

However, with the recent advancements in technologies such as artificial intelligence (AI), auditors can augment their capabilities to sift through and analyze these large amounts of data.

At MindBridge™ Ai, we utilize a hybrid of tests including machine learning based algorithms, rules-based tests, and statistical models,  against each transaction in the complete financial dataset. Through the analysis of 100% of the records, the results are presented to the user in an intuitive, visual interface which augments the capabilities of audit and investigative professionals by allowing them to focus their analysis on the most relevant activities.  MindBridge Ai has elevated the definition of ‘reasonable assurance’ to help auditors ensure financial data is free of errors, and potentially intentional misstatement. Accounting and auditing firms across the world are acknowledging the value of AI and are using it as a competitive advantage.

One such forward-looking firm is KNAV P.A., a KNAV International Limited firm that has recently adopted MindBridge AI Auditor™. KNAV International Limited is a not-for-profit, non-practicing, non-trading corporation incorporated in Georgia, USA, and it has more than 400 professionals and 42 partners in nine locations across the world. I recently sat with Atul Deshmukh, Global Leader of Assurance Services, at KNAV to learn how he plans to transform the auditing business with the use of AI.

Let’s meet Atul!

John Colthart: Tell us about KNAV.

Atul:  KNAV International Limited is an association of global accounting and consulting firms, founded in 1999, that offer a complete suite of public accounting services—including set-up, accounting, assurance, taxation, international transfer pricing, risk advisory and business advisory services—and facilitate seamless business growth through mergers and acquisitions.

Today, KNAV is an international organization comprised of more than 400 professionals and 42 partners in nine locations (India, the United States, Canada, Switzerland, Singapore, France, the Netherlands, the United Arab Emirates and the United Kingdom).

Working with some of the largest and most recognized multinational companies, KNAV enables clients to comply with financial reporting, regulations, and taxation across multiple geographies. KNAV P.A., our US public accounting firm, is headquartered in Atlanta, Georgia and is the US founding member firm of KNAV International Limited. KNAV P.A. is also a member of Allinial Global, an association of independent accounting firms within North America and the rest of the world. The resources of Allinial Global are comparable to those of a “Big Four” firm. Allinial Global firms share ideas, training resources, best practices and, most importantly, knowledge as needed to meet the needs of their clients anywhere in the world. Although member firms are owned and managed independently, there is substantial collaboration among the members, allowing member firms to better serve clients in a wide range of industries.

John Colthart: What do you see as your biggest opportunity?

Atul:  At KNAV P.A., we foresee a tremendous growth in investments by multinational companies, specifically companies from India or in North America and companies that thrive on the Indo-US business model. Our presence in Atlanta and the Bay Area in the US and across various cities in India and our ability to provide audit, tax and advisory services in the US and India to these multinational companies are our biggest opportunities. We have a leadership position in this niche market, and we expect to continue to grow in this market segment.

John Colthart: What do you see as the biggest threat or challenge?

Atul:  The need to keep abreast of changing accounting and reporting standards, the complexity of conducting audits of large public multinational companies and the challenges of ever-changing tax laws continue to be our biggest threats and challenges. Coupled with the increasing complexity within our service delivery, the dearth of skilled and trained resources within our niche segment creates a high-risk environment for our firm. The huge investments that larger accounting firms are making in technology will effectively level the price differentiation between these firms and smaller firms such as ours. Losing our coveted space in our market segment to larger firms is a threat that is real and imminent.

John Colthart: How do you plan to address it?

Atul:  KNAV has established a special task force to keep the firm abreast of the latest developments in accounting, auditing and taxation. We are heavily investing and relying on technology to mitigate the risk arising from the deficiency of skilled and trained professionals in the marketplace. We continuously seek ways to get a first-mover advantage by embracing state-of-the-art tools and techniques that enhance the accuracy and efficiency of our services. In this context, the use of AI for data mining and analytics is a strong step that we have taken to dramatically improve and enhance the efficiency of our professional audit staff.

John Colthart: What made you choose MindBridge Ai Auditor™? What are the features that you plan to use?

Atul:  We at KNAV felt that a unique solution such as MindBridge AI Auditor™ would allow us to provide a more time-efficient, more economical, more rigorous and higher quality audit, which provides an elevated level of assurance to our clients. Since our focus is on having stronger analytical, data science and IT skills to complement our financial and cross-border business acumen, we were clear that this tool would help us tremendously in making our audit process faster, more reliable and more efficient. In today’s digital world, it is critical for the audit profession to embrace advanced technology tools—including robotics, automation, and cognitive technology.

The smart data import, out-of-the-box integration with leading ERP and accounting software, features related to risk-based scoring, user-interface design and transaction sampling are the key features of the MindBridge AI Auditor™ that would enable the KNAV team to work closely with the client. The fact that we are exposing each and every transaction from the client general ledger to 26 rigorous tests is a game changer. Never before have we been able to conduct such an in-depth analysis of client data at the transaction level.

To see an announcement of our relationship with the KNAV P.A., please click here.

How a small public accounting firm plans to use Artificial Intelligence (AI) to power audit assurance and risk analysis

manage audit

Compared to humans, new technologies like Artificial Intelligence (AI) are ideal for automating mundane tasks related to auditing. AI can also verify every transaction and codify human knowledge, enabling auditors to review, analyze and audit more effectively. As big firms adopt AI in their auditing processes, smaller firms are also realizing the value and becoming frontrunners for this positive change in the industry.

Garbelman Winslow CPAs (“Garbelman Winslow”) is a full-service public accounting firm servicing businesses and non-profit organizations for over seventy years throughout the Washington, D.C. metropolitan area, has adopted the MindBridge Ai Auditor™. As part of our Customer Spotlight series, I had the chance to catch up with Samantha Bowling, CPA, CGMA, Partner at Garbelman Winslow. Samantha has been with the firm for over 25 years and has been involved with auditing services since her career started. She has utilized technology to enhance the audit process from paper to paperless and is now implementing AI to assist in risk assessment, audit assurance, and effectiveness.

Let’s meet Samantha!

John Colthart: Tell us about Garbelman Winslow.

Samantha:  We are a small public accounting firm located in Prince Georges County, Maryland.  Our firm has been in existence for seventy years. We provide various services to our clients: Audit, Review, Compilation, Tax, Bookkeeping, Payroll, CFO, Information Technology, Financial Planning and Consulting Services. Our clients include non-profit organizations, construction contractors, manufacturers, professional services, wholesalers, and distributors.  We have utilized technology since our formation starting with a mainframe computer and punch cards to desktops and tablets.  We provide services to other public accounting firms who do not invest in technology so that they can meet their customers’ needs.  We are involved in the accounting profession so that we can make it better for everyone.  We are not afraid of change and embrace technology to enhance efficiency for our office and for our clients.

John Colthart: What do you see as your biggest opportunity?

Samantha: The biggest opportunity I see is to be a part of the development of AI technology to make sure it really does enhance audit efficiency, reduce risk and produce a better audit.  I believe our feedback during this implementation will help make AI relevant and demonstrate to other small firms that AI is available to them.

John Colthart: What do you see as the biggest threat or challenge?

Samantha: The threat is not embracing AI. I feel other small firms will not embrace the technology and they will no longer perform audits.  Auditing services will move to larger firms. In today’s rapidly changing technology environment more and more “CPA” functions are being automated. If CPA’s at small firms stop performing audits, then they will have to redefine their services and relevance.

John Colthart: How do you plan to address it?

Samantha: Implementing MindBridge Ai Auditor™, providing feedback and making sure it works the way we need it to be the first step. Also, exploring additional uses; I believe this is a valuable management tool for outsourced CFO services as well as an oversight tool for all businesses looking to identify risk within their financial transactions. Ultimately using this platform to demonstrate the importance of AI supported services and explaining how it works to all Maryland CPAs.

John Colthart: What made you choose MindBridge Ai Auditor™? What are the features that you plan to use?

Samantha: I was looking for an affordable AI platform.  I used my “Google” machine and this was the only application that I thought would work for my small firm.  We plan to use all features in our implementation phase to assess which may be valuable to each of our clients, not just our audit clients.

To see an announcement of our relationship with Garbelman Winslow, please click here.

The Impact of Artificial Intelligence and Machine Learning on Financial Services and the Wider Economy

business auditor

Recently I was invited to participate as a speaker in the Official Monetary and Financial Institutions Forum (OMFIF) podcast focusing on Artificial Intelligence (AI) and machine learning. OMFIF is an independent think tank for central banking, economic policy, and public investment – a non-lobbying network for best practice in worldwide public-private sector exchanges. This podcast aimed to provide analysis on developments in financial technology, regulation, artificial intelligence and financial inclusion. Below is an excerpt and transcribed version of the podcast.

Interviewer: There is no single definition of artificial intelligence, and it is regularly used as shorthand to talk about everything from chatbots to deep learning. When it comes to financial services, an increasing number of companies across all sectors have been working on creating real-life AI use cases and applications for this range of technologies. The heart of the AI revolution is machine learning algorithms. Software that self-improves it, is fed more and more data. A trend that the financial industry can benefit from immensely. How has AI changed the financial service industry over the past five years and where do you see the greatest application of AI and machine learning algorithms within the financial services sector?

Robin: I think where you see AI being adopted most of all is in places where there are so many big data problems where a normal human can’t cope with the volume and the scale. So, if you take audit as an example, maybe you have a human being looking at transactions to verify if the transactions are good or not and an auditor has to come in and very quickly look at all of the transactions to find out what is going on. One of the coping mechanisms that human beings had for such a situation was doing something called sampling. They take a small set so that they can cope with the volume and verify that those transactions are okay. In that situation, we can train  AI  to look at every transaction and do it in real time as well, and that means that you are not building up a backlog of all transactions to verify. We can codify that human knowledge about what is a valid transaction as well, and we can do that on a vast scale which would not be possible for a human being. So, the biggest disruptive element I think is the ability to codify some degree of human intelligence into these systems and apply them at a vast scale and this is going to cause all kinds of improvements in the quality of activities like auditing. This is applicable everywhere where there’s a lot of data, and there’s a need for us to take some degree of understanding of a problem domain, train an AI system and apply it at scale.

Interviewer: The idea of collaboration is very important between tech and financial services. Robin, as someone who works within an AI company, if you will, what do you see as challenges when it comes to financial institutions adopting the new technology and is there anything that you think can be done to expedite this whole process?

Robin: I see that there are opportunities in building trust in AI and certainly I’ve seen that as one of the big issues that organizations working in the AI field really need to think about. If you think about the types of roles where AI is being used, people like financial accountants, auditors, and even lawyers are being assisted by AI these days and in that environment, quite often they have to justify their actions and what you can’t have is an AI being a black box in that scenario. What you need is an AI system that can explain its workings. I know at MindBridge we spend a lot of time thinking about, as we’re applying algorithms to the areas, how do we explain the findings so that it can support the conclusion. One of the examples is explaining why a transaction is flagged unusual or normal? We took that approach because some of our users can be asked to stand up in a court of law and justify an action that they have taken so they need all of that evidence. So, I think building AI responsibly in a way where they can explain themselves is a very big part of building trust in AI systems.

One of the often-overlooked problems for people working in AI is that they focus on the algorithm and they don’t think about the communication of the outcome. I think that’s one of the big challenges the people who are working in the AI industry need to think about. There is a lot of work going on at the moment in the AI space. Some of the deep learning technology that people are raving about has led a lot of the growth in AI. We need to think about how we take those technologies and turn them into something that the people can understand, and that non-technical people can understand as well. So, I would say that’s one of the biggest barriers to adoption.

Also, smaller firms should be working with the big companies and regulators. A lot of the new technologies are being driven by small, agile innovators and working with regulators or larger organizations helps both sides. From one side the technology matures faster and from the other side you have the awareness of the state-of-the-art, of the possibilities of such technologies are also being conveyed.

To listen to the full podcast by OMFIF, please click the link: https://www.podbean.com/media/share/pb-caaqn-72faa1

Congratulations, you have been entrusted to be the CEO… Now what?

auditing services

Becoming a CEO is like becoming a new parent, there is no true user manual to guide you through the unique challenges you will face along the way. You can read a multitude of books about “how to” raise a child, however, there will inevitably be unexpected curve balls beyond what you already know and what you have read.

Ultimately it is your responsibility to ensure the safety and well-being of your child, under all conditions, 24/7, irrespective of the situation. So, let me start by defining the type of CEO that you are going to be. There are three models to choose from: the “plate spinner,” the “one-man band” and the “conductor.”

The “plate spinner” CEO generally does not have a full management team and by necessity has to rely upon themselves to juggle all, or nearly all, of the functional activities that must be executed by the team. The key drawback is that some tasks may fall between the cracks. It is similar to the “plate spinner” who must run back to the first plate once he has completed spinning the last plate to ensure that the plates do not drop to the floor.

The “one-man band” CEO has a more balanced management team but chooses to address and resolve most functional tasks by themselves. As with the “plate spinner,” this model does not lead to optimal execution or maximize the company’s success.

A great “conductor” can achieve melodious results from the members of their orchestra just by hand motions and facial expressions and without the need to speak a word. Similarly, a great CEO will have their management team working harmoniously. The “conductor” model is the optimal approach in creating an environment inside the company that fosters balanced execution by all members of the team. While the team remains under the guidance of the CEO, this approach leads towards sustainable and predicable growth.

Now that you are familiar with the CEO models, some of which you may be emulating, let me outline the actions that, I believe, a CEO must carry out to be highly successful.

Build the team –The top priority and most fundamental task of the CEO is to find and hire the best management team. The strength of the team will determine the degree of success of the company.

Work on the business, not in the business –This is a very simple notion, to spend most of your time managing and less time doing. Your role is to create an ecosystem for your management team to operate within by defining the vision and setting objectives. Then, create the infrastructure necessary to measure the team’s performance in meeting these goals.

Lead by example – People in the company will follow you if they believe in your vision and your actions. It is as they say that actions speak louder than words. The process is simple; initially, people will award leaders a certain amount of “respect and trust credit” but as time progresses, there could be a drop in the original “trust” level. A leader must build up and maintain his or her “respect and trust credit” by their actions which will earn additional “trust credits”, just as a battery loses its charge overtime and requires recharging. Be mindful that as the battery discharges to lower levels, it may be more difficult, or highly unlikely, to reach full charge again.

Listen – A great leader will do less talking and more listening. After all, you have two ears and only one mouth for a reason. People have the basic need to feel that their voice is heard and as a result will be more engaged and vested in the company’s well-being.

Make decisions – The most basic function of a CEO is to make decisions. A CEO must conduct a proper evaluation and analysis of the actions of their team and company performance even when there is only partial data available before making final decisions. Please remember that “no decision” is a decision in itself.

Think strategically – Wayne Gretzky, arguably one of the most successful hockey players of all time, coined the phrase: “I skate to where the puck is going to be, not where it has been.” Companies have to continuously assess their market position by assembling available data from competitors, current and future product capabilities/performance and market trends. With this data, the team must create scenarios and plan ahead. Based on this strategic thinking the organization then has a guide for tactical execution based on the merit of the potential outcome.

Ultimately, the question is whether these attributes can be acquired, or are they inherent as part of the DNA of the individual. In other words, nature versus nurture. The good news is that most of the above traits can be acquired over time. Given sufficient and consistent practice, an individual can acquire these traits and evolve as a true leader.

Note: Above blog post was earlier posted on Eli Fathi’s blog.

Elevating Reasonable Assurance with the MindBridge Ai Auditor™

assurance audits

With the audit season fast upon us, a person stops to think about ‘reasonable assurance’.

Audit standards such as AU-C 240CAS 240 and ISA 240 stipulate that “…an auditor has a responsibility to plan and perform the audit to obtain reasonable assurance about whether the financial statements are free of material misstatement, whether caused by error or fraud.”

‘Reasonable assurance’ is a term used in industry standards and the audit profession to render an opinion as to whether financial statements are free of error, or in some cases, fraud.

An article published by The Institute of Chartered Accountants in England and Wales (ICAEW) defines ‘reasonable assurance‘ as “…the level of confidence that the financial statements are not materially misstated that an auditor, exercising professional skill and care, is expected to attain from an audit.”

To render a reasonable level of confidence, auditors rely on many factors including the following three key items:

  1. Professional skepticism
    Professional skepticism requires a questioning mind and being alert to conditions which may indicate possible misstatement.
    The Public Company Accounting Oversight Board (PCAOB) standards define ‘professional skepticism as “…an attitude that includes a questioning mind and a critical assessment of audit evidence, and it is essential to the performance of effective audits under Board standards.”
  2. Sample selection of financial transactions
    Given the volume of financial transactions, a sampling methodology is used to analyze the datasets. As it would be labor-prohibitive and not cost efficient to review all transactions manually, a sample selection of the dataset is identified.
    There are two general approaches to ‘audit sampling’: non-statistical and statistical. Both approaches require that the auditor use professional judgment in planning, performing, and evaluating a sample and in relating the evidential matter produced by the sample to other evidential matter when forming a conclusion about the related account balance or class of transactions.
  3. Testing of the selected sample of financial transactions
    Traditional computer-assisted audit techniques (CAATs) that are used to perform tests on financial transactions are typically based upon rules. Rule-based testing requires knowledge of the rule to detect and, in the case of traditional software, technical skills to script the rules. Rules-based systems can’t catch unanticipated scenarios—and, in many cases, the rules themselves can be exploited—making this type of detection increasingly inefficient, not to mention labor-prohibitive to review all transactions manually as mentioned previously.

Each one of the above components is subject to human bias.

As evidenced through countless high-profile cases, and some not so high-profile cases, the fact that clean audit reports were given to many failed or failing companies supports the argument that the current method of asserting ‘reasonable assurance’ must be improved.

In fact, just last week, the U.S. Securities and Exchange Commission (SEC) unanimously approved the PCAOB’s new auditor’s reporting standard, supporting the communication of “critical audit matters” as a way for auditors to provide more information to investors and the public.

As defined in the PCAOB standard and related amendments, critical audit matters are any matter arising from the current period’s audit of the financial statements that was communicated or required to be communicated to the audit committee, and that:

  • Relates to accounts or disclosures that are material to the financial statements, and
  • Involved especially challenging, subjective, or complex auditor judgment.

Further compounding the problem over the last decade are the growth and adoption of:

  • The Internet
  • Hackers
  • ERP systems with multiple touch points
  • Big Data

The combination of these issues further hampers the ability to assert high-levels of confidence that rely solely on human, professional experience.

With the advancements of technologies such as artificial intelligence (AI) that are now available in the market, professionals, including auditors, must augment their capabilities with the power of AI to sift through and analyze large amounts of data.

At MindBridge™ Ai, we remove subjective, human bias from the audit process and perform a full coverage analysis using a hybrid of tests against the complete financial transaction dataset.

The automated analysis of the financial datasets is performed by the MindBridge Ai Auditor™, which combines a hybrid of algorithm-based business rules, statistical models, machine learning and artificial intelligence.

Through the analysis of 100% of the records in the datasets, a ‘risk score’ is generated for each transaction to highlight those that warrant further investigation.

The results are presented to the user in an intuitive, visual interface which augments the capabilities of audit and investigative professionals by allowing them to focus their analysis on the most relevant activities.

By performing an analysis and associating a ‘risk score’ on all financial transactions, coupled with MindBridge Ai’s intelligent statistical sampling capabilities, MindBridge Ai has elevated the definition of ‘reasonable assurance’ to help auditors ensure financial data is free of errors, and potentially intentional misstatement.

Here’s what customers are saying about the MindBridge Ai Auditor.

“The MindBridge Ai Auditor™ enables our team to pinpoint unusual transactions and enhances the thoroughness and precision of our analytics. Investigations that have historically involved months of combing through millions of transaction samples are now much more comprehensive—yet more narrowly focused—and can be concentrated in just hours.” (full text here)

“Rather than our trainees using random number sampling or a sample based on what they deem to be risky transactions, MindBridge ranks all of your transactions on a risk rating, and then depending on whatever the sample size is you pick it gives you, say, the top ten riskiest transactions.” (full text here)

“The machine learning capabilities of MindBridge Ai Auditor are unique in the audit field and go well beyond the capabilities of any data analytics technology we’ve seen for the full audit profession” (full text here)

“MindBridge’s tool demonstrated it can usefully detect anomalies in datasets. Its user interface is intuitive and presented the data visually, allowing the user to explore a time series of each variable, whilst comparing the result to the industry average.” (full text here)

If you’re interested to learn more about MindBridge™ and our Ai Auditor™ platform or would like to run your own A/B test, please visit www.mindbridge.ai.

Making business sense by automating repetitive audit tasks with machine learning and AI algorithms—An interview with Angelique Koopman

audit for company

What better way to start the first post in the “CPAs” series than with a world-class lady. Angelique Koopman, Partner Audit Innovation at Baker Tilly Berk, has been following MindBridge since our early days and recently visited our office. We could not resist asking her a lot of questions.

In this series, we will interview various CPAs in the profession as we travel across the globe, asking them for their opinions and personal stories. If you ever would like to share your own experience as a CPA, please do not hesitate to reach out directly.

Solon:  Where do you think analytics and artificial intelligence (AI) would be the most useful in the audit process today?

Angelique:  Planning and risk assessment are often based on professional judgment. And what I would like to see is the use of more data analytics in the planning phase of the audit so we can substantiate our considerations and our risk assessments with facts (quantitative data). This way we are collecting evidence to support our risk assessments early on.

Solon:  So how should we address that deficiency, and what impact do you think it has during the audit process? What surprised me recently is that there are firms that do it in the review phase, then they miss something, and they have to go back. If you do integrate next-generation AI systems in the planning phase, do you think it would help to decrease this need to re-do portions of the audit?

Angelique:  Yes, when you are doing your risk assessment or your planning, it’s better to have a set of preliminary analytics based on facts. That way, you can substantiate the choices you make which can help you decide to perform all the procedures or just a subset of procedures. Right now, we do risk assessments to limit the audit work. If you do not plan your audit well enough, at the end of an audit, you will find that you missed something somewhere and then you have to go back and do additional work, and I believe this is one of the best arguments for using analytics more in the planning phase.

Solon:  At the end of the day, is it just about analytics, or are there other industry best practices we can also be including?

Angelique:  It is the combination. It’s a combination of automating rigorous procedures and it’s about automating specific aspects related to the industry. I strongly believe in an industry driven approach. It is more of the rule-based part using the industry expertise. But I also believe that analytics must be used in combination with the auditor with all his knowledge and the holistic view of the client. Giving the auditor the technology to do better, to improve professional judgment, to improve decisions based on intuition or feeling and to substantiate those decisions more comprehensively. That is really important. To be honest, I don’t believe in just having a tool or platform only, because, in the end, an auditor needs to evaluate the outcome. But what you can do is help the auditor from performing tedious tasks that could otherwise be automated. Such tools provide the auditor with more insights and knowledge such as making cross-references and analyzing data from more than one dimension. What we normally do with audit analytics, is that we have a question, then we build the query and the query gives an answer that’s different, so I believe in the combination of both analytics and best practices in combination, that it will help auditors become more focused on delivering better results.

Solon:  There is a lot of confusion over the role AI plays in the audit. What would you tell a traditional audit partner who is evaluating if he/she should leverage machine learning and artificial intelligence (AI)? 

Angelique:  Mostly when people hear about AI, they believe that it is science fiction. And they might think it is in the preliminary stage and are not aware of the fact that nowadays lots of things in our society are run by algorithms. We use them everywhere. Unfortunately, we are not using it in the audit on a large scale yet. Maybe, we think it is scary. However, when we get into a plane, we fully trust the airplane, but in the audit world, we do not trust algorithms yet. Basically, it’s about combining statistical methods with machine learning. So I think they need to know what machine learning is and how it works. We have to explain it with simple examples, like how does a program operate and learn? As an example how to identify a cat from a picture? In fact, AI is about automating tasks that are beyond the capacity of human beings. Machine learning is a way of making complex analysis possible. Automating repetitive audit tasks makes even greater business sense when you combine it with machine learning and AI algorithms.

Solon:  People talk about the robot taking the job of an accountant… 

Angelique:  It’s very similar to the team that supports you when you are auditing a bank, and the bank has financial instruments, and you have team members who are specialized in that type of audit; they have the know-how of the financial instruments. Or when you are auditing a real estate company, you use the expertise of people who know that space, you cannot do it by yourself. It’s only expertise enshrined in a computer program. That’s the difference.

I was telling Robin and Eli, knowing that this technology is here, and at this point, it would be hard to go back to audit and do the role I used to do without leveraging this technology. I can clearly see the value it adds, I can do a manual sample, but recognizing that with AI I can sample the whole ledger with a higher degree of assurance. And given that it is my signature on that audit statements, I want to do the best job with the best tools available.

Solon:  So using artificial intelligence (AI) is going to become a matter of professional integrity?

Angelique:  I think, this is something in the audit community which is starting now. And I gave the same message at a recent conference in Belgium. People start to think about it, and they are starting to say, “Yes, why not? Change is coming!”

Originally posted on Solon Angel’s Linkedin post

What’s the big deal about the MindBridge Ai Auditor™?

assurance and auditing

I was enjoying a long successful career of 19 years in the business intelligence and the fintech industry when I met Eli Fathi, CEO of MindBridge Analytics Inc. This is when he introduced me to the Association of Certified Fraud Examiners (ACFE), ‘Report to the Nation’s’ report.

On page 21 looking at detection methods I could not believe that analytics wasn’t really listed and even being generous it was less than 5% of all cases found. As a veteran of the business intelligence and FinTech industry, I was both startled and amazed. Startled that the problem was that large and amazed at the opportunity for MindBridge™ Ai, a company that is willing and able to address this problem by using the latest in learning machine AI technologies on a purpose-built software platform. In addition to the opportunity, it was also the conviction and the commitment of the team, that moved me.

I came to MindBridge Ai to help remove errors and misstatements from financials — period. I felt we could do more to help the Accounting and Finance professionals that I’ve worked with for more than 15 years.

After seeing the MindBridge Ai flagship product — its’ Ai Auditor™ — a world’s first artificial intelligence powered audit analytics software, I thought of my friend Mike. Mike isn’t really his name but the name of any number of the hundreds of the accountants, auditors and finance professionals I’ve met over time. The ‘Mike’s’ I have met, work long hours, spend their valuable time in low cognitive tasks like sifting through volumes of financial transactions and want a better way to conduct audits, while still being rigorous in their approach. This is where MindBridge Ai comes to their rescue because we aim to:

  • Optimize processes and reduce time in mundane tasks — Increased Efficiency
  • Increase their confidence in the figures, processes, and control — Higher Assurance
  • Provide value-added capabilities and services to their organization and clients — Greater Value.

…and do all of this with less staff and reduced pressures!

Knowing we solve these pains I wondered how to best convey them out to the market. In the end, I decided we needed to give ‘Mike’ a voice to share his pains and communicate how we relieve those pains with the MindBridge Ai Auditor™.

Let’s help all the Mikes out there to balance their time and provide higher assurance in their audits.

Statistical sampling- the intelligent way

audit and artificial intelligence

Auditors love statistical sampling and so does the MindBridge Ai team. Why wouldn’t we—statistical sampling uses the laws of probability to measure sampling risk. We truly believe that statistical sampling largely outperforms judgment sampling.

Don’t get us wrong—the experience of a Partner is undisputed and their ability to focus the audit and identify areas of risk in financial statements is the key to a successful audit and, of course, the peace of mind once an audit opinion is issued. However, once the risk areas are identified and a population of thousands (if not millions) of transactions remains to be reviewed, experience alone might not be sufficient to select a test set.

Statistical sampling and the laws of probability make it feasible to pull a test set from a huge population so an audit team can use it to give reasonable assurance that the overall population is free from material misstatements.

Can audit teams give the same reasonable assurance and test fewer transactions?

There are many commonly used methods to enhance statistical sampling in the audit profession. For example, Monetary Unit Sampling is one (very, very popular) way that a test set can enrich the items most likely to be of interest to auditors. In Monetary Unit Sampling, every dollar is regarded as a distinct unit and given equal chance of being in a transaction picked for testing. Therefore, the higher the amount of a transaction is, the more likely it will be part of the test set. In an audit context, this is great as larger transactions, the ones that are more likely to cause a material misstatement, are more likely to be picked for testing.

How did MindBridge come up with an improved sampling methodology?

First, let’s clarify what improved means. For us improved means to enrich data in a meaningful way so it makes the whole testing process more efficient. Similar to Monetary Unit Sampling, we enrich a data set with additional information to make it more likely that transactions get sampled that are of audit interest.

The intelligent sampler

We run a number of tests to calculate a risk score for every single transaction in a data set. This risk score is the basis of our intelligent sampler which pulls a stratified sample set across the whole population. As with Monetary Unit Sampling, every transaction has a chance for being selected for field testing; however, the ones more likely to be of audit interest having a higher chance.

What does “of audit interest” mean?

“Of audit interest” means the likelihood that a transaction is misstated due to fraud or error. In a number of field experiments, the Ai Auditor™ has proven its ability to identify and push fraud- and error-prone transactions to the top of our risk ranking.

This ability of our Ai Auditor™ to identify transactions which are more likely to be fraudulent gives audit teams the ability to reach the desired confidence level with a smaller test set of transactions. MindBridge’s intelligent sampler is a focusing tool that helps the auditor identify audit areas that they should spend the most time on, but at the same time makes sure they also spend enough time in other areas beyond the high-risk transactions.

Comparing statistical random sampling with intelligent sampling

Our data science team created a general journal for educational purposes. This journal consists of 2966 transactions of which 24 transactions are non-compliant. The non-compliance rate in this file is 0.8%.

Assuming an acceptable error rate of 1%, it would be required to test a sample set of 230 transactions to achieve a 90% confidence utilizing statistical random sampling methodology.

Our intelligent sampler takes an equal number of samples from the Low and Medium/High-risk categories. This ensures that every transaction has a chance of being selected in the sample, but those with higher risk have a higher chance. Using this method the auditor has a 90% chance of discovering non-compliance in the file with just testing a sample set of 14 transactions.

Approach Confidence level / error rate Required Sample Size
Statistical Random Sampling 90%/1% 230
Intelligent Sampling 90%/1% 14

 

If you’re interested to learn more about MindBridge™ and our intelligent sampler, or would like to run your own A/B test,  please get in touch.

How MindBridge is Increasing Auditor Assurance

Using MindBridge on a laptop

Throughout my investing and business life, I’ve been amazed by the number of accounting scandals and re-statements that have occurred. The issue of regulatory oversight is raised in the press, but even with increased enforcement and higher penalties, a new accounting issue is never far behind, and it’s continued to get worse. Just look at the impact on British Telecom’s stock because of the irregularities found in accounting in their Italian division (read about it here).

One week into joining MindBridge Ai, I can see how we are focused on the goal of solving one of the massive issues underlying these scandals, and I can see how we are building an expert system to help both internal and external auditors provide us with higher levels of assurance about the financial statements they are sworn to certify.

And the baseline issue we are dealing with is the amount of financial data they need to consume. There has always been too much of it. The Big Data problem facing auditors is that they are asked to “go fishing in a murky lake” and find accounting anomalies and irregularities for further review. To this point, accountants and regulators are agreed that this should be done using statistical account sampling. However, the quality of the sample can only be measured after it has been sampled and analyzed. And with the time pressure on providing audited results, if an irregularity or anomaly is found, the process has to start again. And this leads to costly re-statements which impact shareholder value and confidence in the audit firms.

To solve this, Mindbridge Ai is leveraging machine learning AI to provide auditors with a more highly justifiable sample before the audit begins. Our Auditor AI service can ingest and analyze the entire leger and pinpoint areas they should be investigating early on with a higher degree of justification against the standard, and currently accepted sampling techniques. This gives auditors a higher assurance rate and helps them improve upon the current approaches that only meet the standard, and not move past it and towards the answers we, the press and firms like British Telecom need.