Berenberg, one of Europe’s oldest and leading privately owned banks, sought a faster and more reliable way to review its ICT third-party service provider contracts against the requirements of the Digital Operational Resilience Act (DORA). With DORA compliance becoming mandatory by January 2025, Berenberg needed a solution that would automate contract reviews, ensure DORA alignment, reduce costs and errors, support the register of information, and accelerate contract renegotiations. The AI assistant led to a 70% increase in processing speed, 80% reduction in manual effort, with an accuracy rate of 90-95%.

“The AI assistant for contract review has significantly accelerated our approach to DORA compliance. Its automated clause-by-clause analysis quickly identifies discrepancies, suggests relevant amendments, automatically generates addenda for renegotiation with our ICT third-party service providers and extracts relevant contract data for reporting to the information register—all with remarkable accuracy. By reducing manual effort by 80%, we have significantly reduced both, our costs and resource bottlenecks, allowing our team to focus on higher value tasks.”

Alexander Martens, Procurement / Provider Management at Berenberg

The majority of the profit in modern retail banking comes from loan products. Unfortunately, not all of your clients borrow money from you. Our data science model is built to detect all payments going from the bank to other banks and financial institutions to pay for existing loans elsewhere. The whole instalment loan battlefield can be laid open in front of your eyes. This is a perfect opportunity to offer clients a loan transfer or consolidation through a bank. You find more about the use case at bigdataforbanking.com.

In telecommunications as well as banking, you have enough data to reconstruct family relationships between clients. Based on data concerning card payments, phone calls, and location, you can correlate people in time and space. If you add some simple demographics, you end up with a surprisingly precise data-science model that finds a person’s spouse, parents, siblings, etc. In some countries, this sort of data mining cannot be legally employed, but in others, regulations are not so strict. So, the bank can use this knowledge to improve a person’s risk score based on her family members. Thus a telecommunications operator or a bank can make a better-targeted marketing offer.

Setting interest rates is a complicated problem. If you set rates too low, you are cutting your profit. If you set them too high, your clients won’t accept them or will soon move on. The competition is very stiff, especially online. Most banks use risk-based pricing, assigning each class of clients interest rates according to the probability of default. This method can be improved by data-science algorithms. By giving clients who are more price sensitive a little bonus at the expense of clients who are less price sensitive while maintaining the average interest rate for all risk classes, we can improve acceptance, reduce turnover, and boost profit while still complying with strict banking regulations. Find more in the video or read about the use case at bigdataforbanking.com.