AI in banking: Making the case

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Self-driving cars on our streets, Siri in our pockets, Alexa in the living room ... artificial intelligence and the machine-learning process behind it is already in use today.

It is partly embedded in our everyday life, and partly still has a “wow” factor about it. For example, when we hear that the human ‘driver’ of a Tesla self-driving sports car in Florida died in a collision with a truck that the car’s autopilot system failed to spot.

But where is AI being used in banks? Robot Process Automation (RPA), for example, is replacing processes behind the scenes in several banks. The time- and cost-pressure in this industry is high, and not just because of the ongoing global financial crisis and the regulations that have developed out of it. New market entrants are also making life more difficult for traditional houses. Their models are bank-like, but they are able to offer more favourable international transfers and bring bank transactions to smartphones. These young financial firms are challenging the big money bureaus in their most lucrative areas of business.

Practical questions

“We have collected 30 use cases — many areas have been surveyed — and now we are looking at these and evaluating them,” the CIO of one of the top ten banks in Germany told me (Top Ten after balance sheet total, 2016). He is using new innovative forces from Silicon Valley to help in the evaluation — and, of course, with the opportunities that are expected to arise from it.

But there are still so many questions. Will using chatbots be compliant with regulations? Could AI be helpful when selecting my investment strategy for securities? I often hear these or similar questions in discussions about machine learning. Will it be acceptable? Apparently so. Critically, many banks have recognised the need to put data in the cloud and to keep the cost of the production of AI models under control.

Will using chatbots be compliant with regulations? #AI #banking Click To Tweet

What is now holding up the hype around AI? Which is also being pushed by numerous advertising videos and on YouTube? According to various press reports, Watson, the alleged AI leader, recently became the first program to take part in a state tender for the fight against cyber-terrorists in Italy. However, a report in the US caused concern that Watson was overburdened with the evaluation of cancer data, and it all went a bit quiet. Perhaps we are still not quite confident in using AI routinely.

I have regular conversations with a friend who has high hopes that AI will have a lot of potential in the area of ​​risk management, despite the lack of evidence. But there are many equally innovative and hopeful employees in the banking sector. How quickly will we hear about the breakthrough with AI in banking: the “real” use case with measurable benefits?

Generating measurable benefits

Let’s take a look at the benefits that a bank may achieve from the use of AI. If we ask bankers what they think will be the most significant advancement of the next 20 years, most will almost certainly mention AI.

In the ERP environment, simple processes will be replaced by thinking machines. They are able to simulate and automate an empirical treatment of financial documents, such as the use of the correct booking rate on the basis of tax-relevant documents. Machines can already take over many of the small core tasks of traditional banking. What’s more, they are more reliable and accurate than a human being. Machine learning procedures also score when it comes to a model with closely connected attributes and huge quantities.

One reason for this is the way in which AI-enabled systems deal with data. High-performance and intelligent automation, which must also be fully digitalized, is required. Most routine processes in a bank require a large amount of data. The banks have to be able to process this information efficiently, because of the complex intertwined regulations of banking supervision and customer-specific conditions and constraints.

An essential requirement?

As if this were not challenging enough, the sheer quantity of data to be processed is exploding. At the same time, the legislation on data protection and how we process it (GDPR) is changing. There is very little question that banks need AI solutions, if only to be able to make effective use of unstructured data, and gain insights from it.

There is very little question that banks need #AI solutions... Click To Tweet

Enterprise readiness for AI

SAS recently conducted 100 interviews with business leaders to understand the current state of AI readiness in corporations. You can read the full SAS report here.

This blog post was first published in German on the regional SAS blog Mehr Wissen.

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About Author

Christian Engel

Head of Pre-Sales Banking, DACH region

Christian Engel has lead a group of strategic business analytics advisors for key SAS accounts since 2006. His academic background is in mathematics and he completed his Diploma degree with concentrations in Operations Research in 1996 in Darmstadt. Christian’s professional career began in the banking sector but he currently works across all industries in Germany. His day-to-day work involves calculating the value contribution of analytics software, optimizing analytic platforms for departments, and innovation projects related to new software technologies.

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