On Digital Transformation, Big Data, Advanced Analytics, AI for the Financial Sector. Interview with Kerem Tomak
“A lot of times we think of digital transformation as a technology dependent process. The transformation takes place when employees learn new skills, change their mindset and adopt new ways of working towards the end goal.”–Kerem Tomak
I have interviewed Kerem Tomak, Executive VP, Divisional Board Member, Big Data-Advanced Analytics-AI, at Commerzbank AG. We talked about Digital Transformation, Big Data, Advanced Analytics and AI for the financial sector.
Commerzbank AG is a major German bank operating as a universal bank, headquartered in Frankfurt am Main. In the 2019 financial year, the bank was the second largest in Germany after the balance sheet total. The bank is present in more than 50 countries around the world and provides almost a third of Germany’s trade finance. In 2017, it handled nearly 13 million customers in Germany and more than 5 million customers in Central and Eastern Europe. (source: Wikipedia).
Q1. What are the key factors that need to be taken into account when a company wants to digitally transform itself?
Kerem Tomak: It starts with a clear and coherent digital strategy. Depending on the level of the company this can vary from operational efficiencies as the main target to disrupting and changing the business model all together. Having clear scope and objectives of the digital transformation is key in its success.
A lot of times we think of digital transformation as a technology dependent process. The transformation takes place when employees learn new skills, change their mindset and adopt new ways of working towards the end goal. Digital enablement together with a company wide upgrade/replacement of legacy technologies with new ones like Cloud, API, IoT etc. is the next step towards becoming a digital company. With all this comes the most important ingredient, thinking outside the box and taking risks. One of the key success criteria in becoming a digital enterprise is the true and speedy “fail fast, learn and optimize” mentality. Avoiding (calculated) risks, especially at the executive level, will limit growth and hinder transformation efforts.
Q2. What are the main lessons you have learned when establishing strategic, tactical and organizational direction for digital marketing, big data and analytics teams?
Kerem Tomak: For me, culture eats strategy. Efficient teams build a culture in which they thrive. Innovation is fueled by teams which constantly learn and share knowledge, take risks and experiment. Aside from cultural aspects, there are three main lessons I learned over the years.
First: Top down buy-in and support is key. Alignment with internal and external key stakeholders is vital – you cannot create impact without them taking ownership and being actively involved in the development of use cases.
Second: Clear prioritization is necessary. Resources are limited, both in the analytics teams and with the stakeholders. OKRs provide very valuable guidance on steering the teams forward and set priorities.
Third: Building solutions which can scale over a stable and scalable infrastructure. Data quality and governance build clean input channels to analytics development and deployment. This is a major requirement and biggest chunk of the work. Analytics capabilities then guide what kind of tools and technologies can be used to make sense of this data. Finally, integrating with execution outlets such as a digital marketing platform creates a feedback loop that teams can learn and optimize against.
Q3. What are the main challenges (both technical and non) when managing mid and large-size analytics teams?
Kerem Tomak: Again, building a culture in which teams thrive independent of size is key. For analytics teams, constantly learning/testing new techniques and technologies is an important aspect of job satisfaction for the first few years out of academia. Promotion path clarity and availability of a “skills matrix” makes it easy to understand what leadership values in the employees are important and provides guidance on future growth opportunities. I am not a believer in hierarchical organizations so keeping job levels as low as possible is necessary for speed and delivery. Hiring and retaining right skills in the analytics teams are not easy, especially in hot markets like Silicon Valley. Most analytics employees follow leaders and generally stay loyal to them. Head of an analytics team plays an extremely important role. That will “make it or break it” for analytics teams. Finally, analytics platforms with the right tools and scale is critical for the teams’ success.
Q4. What does it take to successfully deliver large scale analytics solutions?
Kerem Tomak: First, one needs a flexible and scalable analytics infrastructure – this can comprise on-premise components like a Chatbots for example, as well as shared components via a Public Cloud. Secondly, it takes an end-to-end automation of processes, in order to attain scale fast and on demand. Last but not least, companies need an accurate sense of customers’ needs and requirements to ensure that the developed solution will be adopted.
Q5. What parameters do you normally use to define if an analytics solution is really successful?
Kerem Tomak: An analytics solution is successful if it has a high impact. Some key parameters are usage, increased revenues and reduced costs.
Q6. Talking about Big Data, Advanced Analytics and AI: Which companies are benefiting from them at present?
Kerem Tomak: Maturity of Big Data, AA and AI differs across industries. Leading the pack are Tech, Telco, Financial Services, Retail and Automotive. In each industry there are leaders and laggards. There are fewer and fewer companies untouched by BDAA and AI.
Q7. Why are Big Data and Advanced Analytics so important for the banking sector?
Kerem Tomak: This has (at least) two dimensions. First: Like any other company that wants to sell products or services, we must understand our client’s needs. Big Data and Advanced Analytics can give us a decisive advantage here. For example – with our customers’ permission of course – we can analyze their transactions and thus gain useful information about their situation and learn what they need from their bank. Simply put: A person with a huge amount of cash in their account obviously has no need for a consumer credit at the moment. But the same person might have a need for advice on investment opportunities. Data analysis can give us very detailed insights and thus help us to understand our customers better.
This leads to the second dimension, which is risk management. As a bank we are risk taking specialists. The better the bank does in understanding the risks it takes, the more efficient it can act to counterbalance those risks. Benefits are a lower rate of credit defaults as well as a more accurate credit pricing. This is in favor of both the bank and its customers.
Data is the fabric which new business models are made of but Big Data does not necessarily mean Big Business: The correct evaluation of data is crucial. This will also be a decisive factor in the future as to whether a company can hold its own in the market.
Q8. What added value can you deliver to your customers with them?
Kerem Tomak: Well, for starters, Advanced Analytics helps us to prevent fraud. In 2017, Commerzbank used algorithms to stop fraudulent payments in excess of EUR 100 million. Another use case is the liquidity forecast for small and medium-sized enterprises. Our Cash Radar runs in a public cloud and generates forecasts for the development of the business account. It can therefore warn companies at an early stage if, for example, an account is in danger of being underfunded. So with the help of such innovative data-driven products, the bank obviously can generate added customer value, but also drive its growth and set itself apart from its competitors.
Additionally, Big Data and Advanced Analytics generate significant internal benefits. For example, Machine Learning is providing us with efficient support to prevent money laundering by automatically detecting conspicuous payment flows. Another example: Chatbots already regulate part of our customer communication. Also, Commerzbank is the first German financial institution to develop a data-based pay-per-use investment loan. The redemption amount is calculated from the use of the capital goods – in this case the utilization of the production machines, which protects the liquidity of the user and gives us the benefit of much more accurate risk calculations.
When we bear in mind that the technology behind examples like these is still quite new, I am confident that we will see many more use cases of all kinds in the future.
Q9. It seems that Artificial Intelligence (AI) will revolutionize the financial industry in the coming years. What is your take on this?
Kerem Tomak: When we talk about artificial intelligence, currently, we basically still mean machine learning. So we are not talking about generalized artificial intelligence in its original sense. It is about applications that recognize patterns and learn from these occurrences. Eventually tying these capabilities to applications that support decisions and provide services make AI (aka Machine Learning) a unique field. Even though the field of data modelling has developed rapidly in recent years, we are still a long way from the much-discussed generalized artificial intelligence which had the machine goal outlined in 1965 as “machines will be capable, within twenty years, of doing any work a man can do”. With the technology available today we can think of the financial industry having new ways of generating, transferring, accumulating wealth in ways we have not seen before all predicated upon individual adoption and trust.
Q10. You have been working for many years in US. What are the main differences you have discovered in now working in Europe?
Kerem Tomak: Europeans are very sensitive to privacy and data security. The European Union has set a high global standard with its General Data Protection Regulation (GDPR). In my opinion, Data protection “made in Europe” is a real asset and has the potential to become a global blueprint.
Also, Europe is very diverse – from language over culture to different market environments and regulatory issues. Even though immense progress has been made in the course of harmonization in the European Union, a level playing field remains one of the key issues in Europe, especially for Banks.
Technology adoption is lagging in some parts of Europe. Bigger infrastructure investments, wider adoption of public cloud, 5G deployment are needed to stay competitive and relevant in global markets which are increasingly dominated by US and China. This is both an opportunity and risk. I see tremendous opportunities everywhere from IoT to AI driven B2B and B2C apps for example. If adoption of public cloud lags any further, I see the risk of falling behind on AI development and innovation in EU.
Finally, I truly enjoy the family oriented work-life balance here which in turn increases work productivity and output.
Dr. Kerem Tomak, Executive VP, Divisional Board Member, Big Data-Advanced Analytics-AI, Commerzbank AG
Kerem brings more than 15 years of experience as a data scientist and an executive. He has expertise in the areas of omnichannel and cross-device attribution, price and revenue optimization, assessing promotion effectiveness, yield optimization in digital marketing and real time analytics. He has managed mid and large-size analytics and digital marketing teams in Fortune 500 companies and delivered large scale analytics solutions for marketing and merchandising units. His out-of-the box thinking and problem solving skills led to 4 patent awards and numerous academic publications. He is also a sought after speaker in Big Data and BI Platforms for Analytics.
Commerzbank AG Big Data and Advanced Analytics Events, MeetUp, Frankfurt, Germany
On European data protection — interview with Giovanni Buttarelli, Digital Initiative, Harvard Business School, June 5, 2019
On the Future of AI in Europe. Interview with Roberto Viola, ODBMS Industry Watch, October 9, 2018
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