You can find this article and others back on AQ Boost site.
AQ Boost interviewed Pieter de Kok, Chartered Accountant and Founding Partner of Coney. Coney is an independent, data-driven, consulting and audit firm, with offices in the Netherlands and Belgium. It provides services in Financial Auditing, Business Analytics and Continuous Monitoring.
Pieter has always been fascinated by the power of data. His face lights up when he talks about how he, a young Accounting graduate, met his “hero”, Professor Hans Verkruijsse who was predicting the Artificial Intelligence revolution in Audit. Pieter joined the Ernst & Young Audit team of Rotterdam in 1995. In 1999, he became Chartered Accountant and won a prize for his essay on e-commerce and real-time auditing. After some years of international assignments in Silicon Valley among dotcom companies and in London, Pieter got more and more interested in developing data driven audit services.
Taking matters in his own hands, Pieter left E&Y and co-founded Coney in 2005. Over the years, he could successfully realize his vision: expanding services in Audit and Tax, partnering with leading Data Analytics software companies, creating a Training Academy and developing a cloud analytics platform to offer Analysis-as-a-Service.
Pieter keeps a relentless drive to innovate and rock the Audit Profession with data analytics. See his LinkedIn profile.
What is your approach when it comes to innovative digital technologies?
The key is stimulating innovation while looking for practical use cases. Our approach is to work with technical experts who help us define use cases, to practice and move ahead.
Take process mining. This technology uses transaction logs coming from ERP systems to build a description of how Business Processes effectively run in your company. Based on that, you can check compliance, identify bottlenecks and recommend process redesigns.
We started investigating Process Mining back in 2011 with students from Eindhoven. We were among the first outside the academic field trying to implement the technology. After three years, it came to its fruition as an established service used by our Analysts.
On Blockchain: we stay out of the hype but still, we try to establish concrete use cases. We are helped by two graduates from Delft University, who understand the technology.
We approach Machine Learning the same way. There is a lot of marketed hype around this topic, but no one really knows where to start. Last year, we organized a round table with start-ups developing Artificial Intelligence applications in Audit. The result was disappointing. Besides the nice interface, the applications we saw were based on scripts similar to what we had been using for the last 20 years. Basically, these are descriptive statistical analytics, but no self-learning algorithms. I am convinced we are moving in the right direction, but it will take time.
Which new digital technology is the most promising for Audit Services?
We are putting our efforts in understanding the power of Machine Learning. We already master the use of Descriptive Data Analytics. It consists in defining risk indicators and control objectives programmed into a script. We run those scripts to review hundreds of millions of transactions and get findings. But here, we already know what to look for.
The future of Auditing is the integration of Machine Learning applications to supplement the Auditor’s judgement and obtain findings we could not have found by ourselves.
There are, however, a lot of practical questions to address. Firstly, we need to develop specific training datasets to establish the learning models. To do that, we need a massive amount of data, which are not publicly available.
Secondly, as an Audit firm, we need to justify why the algorithms came up with findings. It is easy with descriptive analytics because we explicitly program the rules, whereas neural networks are a black box. It will take time to overcome these points. We have hired Machine Learning specialists to help us develop the platform we may use five years from now.
How to you see the maturity level of companies in their use of Advanced Analytics?
There is still a large group of companies that are benefitting from traditional data analytics just to assess data quality and get basic information. A second group of companies use data analytics to improve the control of their processes and transactions. Then, there is a third group, which truly understand the value of data for their business. These companies play in the Champions League. They have a sense of urgency to use data to improve their services and get competitive advantage.
Our ambition at Coney is to help these ambitious companies build data driven business models. Two years ago, I was approached by large insurance company. They were interested in integrating brokers, insurance companies and experts in Damage Control and Assessment. They asked me to help them develop a business case on how the proper use of data could improve Control and Risk Management throughout the whole chain. We published a White Paper on the Future of Insurance.
What are the critical success factors for a Data Analytics project?
One of the key conditions I am always looking for is my client’s commitment to do something with the analysis results. Quite often, we are asked to conduct analytics without any organized follow-up and action plan. I am not interested in quick fix projects, because a Shareholder, a bank or a Supervising Committee ordered it.
Another critical element is the client’s willingness to be transparent and provide all data required. This can prove a challenge when politics is at play between different departments.
How will the role of Finance & Audit professionals change?
I’ve been struggling with this question over the past few years. Of course, we do hire Accounting and Audit graduates and train them on data analysis practices. However, this has not always worked. People choose to be Auditors for some reason. Most of them are the “blue-personality” type, versed in framework thinking, legislation and control. Very valuable, but not always well suited for performing data-analytics. I learned that it is very difficult to convert them into data driven professionals.
I found out it is just as valuable to find Data Analysts who can be trained in Audit and Finance. For example, we have hired a graduate in Astronomy with a strong data analysis background. We put him in the audit team and he was perfectly able to audit complex processes. Data Analysts understand how data are telling the story of business success or failure. They can perfectly be integrated in the audit team and are very well equipped to assist the Auditors.
What is your advice for Finance Professionals to boost their Analytics Quotient?
It is important to get an understanding of how technology will benefit their role. At Coney Academy, we organize inspirations sessions where Finance Professionals can talk to peers and understand Use Cases on Data Analytics and Process Mining. I don’t advise to follow, say, a 4-day technical course on how to script. Only few people will eventually like it and be ready to invest the time to go in depth. That requires complete different skills sets.
Ultimately, it is a question of team work. Analysts can quickly run the mining, but you need the Controller to understand what you see.
Be open to share and learn. The technology is so complex that we need each other’s experience. We need to become a more open community. If everybody has that mindset, we can all benefit and go faster.
What do you recommend that has recently inspired you?
I read books that present the future of technology, but what truly inspires me are the stories about practical use cases. Last year, for example, KPN received an award from the Institute of Management Accountants. As part of a global finance transformation program, they put in place a control framework and monitoring dashboard used throughout all the company. This is an inspiring example of a real, practical use case combing different technologies: Data Mining, Data Visualization, Storytelling.