November 2017

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

December 18, 2020November 22, 2017 by John Colthart

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

December 18, 2020November 15, 2017 by Robin Grosset

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?

December 18, 2020November 8, 2017 by Eli Fathi

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™

December 18, 2020November 1, 2017 by Jim Fagan

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

Audit standards such as AU-C 240, CAS 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:

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.”
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.
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.