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How to Implement AI in Banking Today | FF News with ING

How to carefully, and effectively implement AI in banking today.

We sat down with Bahadir Yilmaz, Chief Analytics Officer at ING, to find out how they’re using AI, machine learning, and advanced analytics to enhance customer service, streamline lending, and support ethical banking practices. 

ING are one of a number of banks turning to the blossoming tech to revamp chatbots and enhance fraud detection and AML, alongside other use cases. Find out how they’re using it and the considered process they’re going through to make it work in this great interview.

Making AI work for everyone

ING are a huge European banking group, active in ten retail and forty wholesale markets, so any technological development they embark on is going to make a splash. Yilmaz oversees a team of around 400 professionals, including data scientists, data engineers, and product managers, all working to leverage data and technology to improve ING’s services. There’s clearly a desire to make AI work for everyone. 

Unsurprisingly one of the main use cases for AI is in developing better chatbots and enhancing customer service. ING are turning to this area of development too. In the video above, Yilmaz explained that while contact centers remain a staple for financial institutions, the underlying customer goal is always quick problem resolution, not simply engaging with customer service. ING has explored chatbot technology since 2017, initially relying on statistical NLP-based approaches. 

But the emergence of advanced large language models allowed the bank to transition to a more sophisticated AI-driven model, drastically improving its chatbots’ responsiveness and accuracy. This shift has positively impacted ING’s automation rates and customer satisfaction, demonstrating how effective and responsive chatbots can meet customers’ demands for instant solutions.

Beyond Chatbots

The bank’s AI strategy extends beyond chatbots, applying machine learning and advanced analytics across various domains, from lending and pricing to marketing, fraud detection, anti-money laundering, and sustainability initiatives. There are potentially a number of benefits in the area of lending, where ING uses transactional data to offer customers tailored, faster, and more objective lending solutions. 

This approach also reduces biases in decision-making, promoting inclusivity by removing human subjectivity and discriminatory elements from lending models. So it’s not just about using AI to increase the speed and personalization of loans but also impact fairer access to financial services.

There are of course widespread concerns about AI’s ethical and security implications which Yilmaz says are very much on the bank’s radar. He shared that implementing AI responsibly involves more than merely applying technology to solve business problems. For ING, 95% of the work begins after initial AI deployment, as the bank integrates comprehensive risk management systems to ensure security, accuracy, and fairness. 

They also have a rigorous 20-step AI review process that assesses each system against 140 potential risks. Only when all risks are adequately mitigated does the bank approve a solution for production. This extensive oversight reflects ING’s commitment to safe, bias-free, and ethically sound AI applications, establishing a trust framework critical for both the organization and its customers.

Where people come in

Yilmaz also discussed the shifts needed in workforce skills as AI transforms business operations. One core area of transition is training more professionals in AI development and implementation, particularly for generative AI technologies. A second area of workforce transformation focuses on evolving roles as AI takes on more information-processing tasks. For instance, in anti-money laundering, where employees traditionally analyze financial records, AI will increasingly perform initial assessments, leaving employees to verify AI-generated insights. This shift demands new skills and mindsets, as employees adjust from manual data extraction to evaluating and overseeing AI-generated information—a fundamental change in daily operations that ING is actively supporting through targeted upskilling and training programs. It’s a big shift!

Be sure to check out the rest of this must watch video, adding to a number of other interviews on this important topic. You can watch all of them and more, right here

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