How AI is changing banking
Artificial intelligence is considered one of the technologies that can fundamentally change industries. Banking is no exception. We show three possibilities – from investing to ESG to financial crime.
The principle sounds simple: if you know your customers well, you can make suggestions that best suit them. This wisdom, which has always applied in the analogue world, is becoming increasingly important in the digital world. It could also become more important when advising on investments in the future.
For example, algorithms can help bank advisors find funds, bonds or shares that suit customers. "Anyone who has ever shopped on an online marketplace is familiar with such product suggestions," says Max Mindt, who is driving the "Next best offer" project for Deutsche Bank. "It's just much more difficult to implement in investment because it's much more regulated."
Anyone who has ever shopped on an online marketplace is familiar with such product suggestions
But at the same time, the principle also offers enormous opportunity. With "Next best offer", for example, algorithms continuously analyse the portfolios of Wealth Management Clients customers for risks. If, for example, a bond is downgraded, analysts issue a sell recommendation, or a region is particularly heavily overweighted, then the algorithm shows the advisor a warning.
When does the switch recommendation come?
The warning comes with a product suggestion to minimise the customer's risk. The algorithm takes this product suggestion from the portfolios of comparable customers. "At this point, we thought about it for a long time and tried it out a lot," says Kirsten Bremke, who came up with the original idea for "Next best offer" and now manages it. "In fact, customers prefer products that other comparable people already have. That's when they're most likely to switch."
There is only a recommendation for a switch if this switch delivers a high added value to the customers. After all, switching also costs money. "Our algorithm checks that the expected benefits of switching exceed the costs," says Bremke. "After that, the advisors then decide whether to actually pass the proposal on to the customer – after all, they're the ones who know our customers best."
Our algorithm checks that the expected benefits of switching exceed the costs
At the moment, "Next best offer" is being used in Germany. The next step will be to roll it out in Italy, Spain and Asia. The model is constantly learning with Bremke and Mindt closely monitoring how customers react to suggestions and using their findings to train the model, which then improves its suggestions.
Tracking down criminals with artificial intelligence
There are also models for combating financial crime that are constantly learning. Here, the AI model "Black Forest" analyses transactions and records suspicious cases. For each movement of capital, for example, various criteria are examined, such as the amount, the currency, the country to which it is going and the type of transaction: was it made online or over the counter?
If a criterion does not match the typical patterns, "Black Forest" reports the anomaly to the account manager. If he also finds the transaction suspicious, he forwards it to the Anti-Financial Crime department. As the feedback increases, the AI learns to classify transactions correctly and only report those where there is a real threat of a crime.
Such AI models (…) help keep up with the huge challenge of fighting crime.
The "Black Forest" model has been in use since 2019 and has already uncovered various cases including one related to organised crime, money laundering and tax evasion. "Such AI models are quite flexible and thus a good complement to existing systems," says Thomas Graf, who built "Black Forest." "They can process large amounts of data quickly and thus help keep up with the huge challenge of fighting crime."
How AI is helping banks support sustainability transformation
The ability to quickly process large amounts of data makes AI models attractive to other fields such as sustainability, for example. From 2023, European Union banks will have to publish which transactions are green.
To do this, they will be guided by the EU's classification, which states which loans for solar and wind power generation, for example, are considered green. Financing for a medium-sized company to invest in equipment or systems that will make it more climate-friendly is also green. To properly classify the transactions, banks need a lot of new data from their corporate customers.
“Until now, this customer data has had to be examined individually by advisors,” says Murat Cavus, who is developing new technologies to support Deutsche Bank's sustainability efforts. In the future, machine learning can help classify deals as green. The algorithm will then make a pre-selection. This process is called autoclassification. “With autoclassification, we would take an enormous amount of work off our customer advisors,” Cavus continues.
We will use more and more technology that shortens standard processes and has a low carbon footprint
True, in the end, a human still has to approve the algorithm's suggestion. But: "Autoclassification provides additional information that makes the final decision easier," says Cavus. This is also how he envisions the future of AI at Deutsche Bank in general: "We will use more and more technology that shortens standard processes and has a low carbon footprint," he says. "This will allow us to better serve our customers and become greener as a bank ourselves."
About Kirsten Bremke
Kirsten Bremke and her team develops data infrastructure, information systems and data science applications for the International Private Bank. She started her career at Deutsche Bank in 1997 as a credit risk manager, then worked for an international consulting firm and has held various management positions since returning to Deutsche Bank, first in Wealth Management and later in the IPB.
About Murat Cavus
Murat Cavus joined Deutsche Bank in February 2021 and focuses on data and technology topics around ESG. As part of the Cloud and Innovations network, Technology, Data and Innovation, he leads a group focused on the development of ESG platforms and their monetisation.
About Max Mindt
Max Mindt joined Deutsche Bank in 2015 and works as a Senior Data Scientist in the International Private Bank. He develops innovative algorithms using artificial intelligence techniques. His focus is in the areas of client engagement, cost-benefit analysis for portfolio rebalancing and generation of investment proposals.
About Thomas Graf
Thomas Graf has been working as a Senior Data Scientist in Wealth Management, in the International Private Bank since 2017. He develops advanced algorithms using artificial intelligence and machine learning techniques. In doing so, he focuses on projects that are regulatory requirements such as automated monitoring of customer transactions and other activities.
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