ndgit series: Experts talk about banking disruption
Kevin Smith is Head of Analytics & AI at Contovista. As a data scientist, Kevin Smith specialises in analysing data using AI methods and extracting actionable insights from it. In this interview, he answers three questions about the opportunities that data-driven banking affords for medium-sized banks.
Why are banks destined to implement data-driven business models?
Banks have always been organisations in which mathematical models and data formed the basis for business processes. As a result of widespread public debate about Big Data and AI, the topic of data-driven banking is gaining a lot of attention; and rightly so. In contrast to individual tech providers that get to know their customers through a single platform or product, banks learn much more about their customers’ habits, which they can then use to sustainably increase customer retention and share of wallet.
Can you give some concrete use cases?
First of all, banks have transaction data from the banking system at their disposal. In addition, there are credit card transactions and possibly other data from second and third-party banks. Now that PSD2 is in force, customers can instruct their house bank to use this data as well. This has resulted in over 200 data categories, geoinformation, personal interests and much more, which we extract out of the transactions. With this information, a bank can target customers and offer a product that is tailored to their individual circumstances. For example, those who buy lots of baby products may be interested in financing their own home. Likewise, someone who frequently travels will probably appreciate a credit card, travel insurance or foreign currency account. Customers who just received a salary increase or often transfer money to other bank accounts or investment platforms might be interested in investment products.
Finally, thanks to Machine Learning, we can identify these relationships (and much more complex ones) automatically instead of having to define them manually.
What would you recommend to medium-sized banks that want to enter the world of AI-based banking?
We have noticed that people often talk too much about algorithms and not enough about the data behind them. In order to reap measurable benefits from machine learning, banks should invest much more time in dealing with data itself. This is because consolidating and refining data from relevant sources is the most solid foundation for any AI project. It is worth investing enough time at the beginning of the project if you want to achieve the desired benefit. Additionally, I recommend that you use clear KPIs to formulate concrete objectives such that the success of a project can be measured at the end.
Here you can find the latest whitepaper of Contovista about “Three strategies for implementing data-driven banking with the help of AI”.
About Contovista
Contovista enables data-driven banking. Our white-label software and data analytics services allow financial institutions to optimize their digital banking experience and gain actionable customer insights. We help to understand, engage and serve customers better based on the enrichment of financial data and machine learning. This empowers organizations to increase customer retention and share of wallet. Contovista is headquartered in Zurich, Switzerland. www.contovista.com
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