Artificial Intelligence in Financial Services – In Data We Trust

This past year has been a busy one for emerging technologies, especially in regard to artificial intelligence (AI). AI startup funding reached a record high of US $7.4 billion during the second quarter of 2019, according to CB Insights. From healthcare to financial services, from digital surveillance to education, new use cases continue to emerge – along with headlines on the dangers and opportunities of such concentrated power. This is hardly surprising, given an increasingly digital economy and that our daily lives are increasingly powered by personalized data.

Transforming financial services

Slowly, but surely, AI is beginning to transform financial institutions’ relationship with their customers, as well as their relationship with money. From mobile banking, fraud detection, anti-money laundering, customer service, and beyond, incumbents and startups have been deploying various tools that aim to improve operational efficiencies and to provide a seamless customer experience.

Combined with advanced data analytics, financial institutions can now provide contextual insights and empower consumers to set their finances on auto-pilot towards a desired goal, based on historical transaction data and a holistic picture of the consumer financial well-being. No wonder many in the business consider AI to be a must-have technology in order to remain relevant and competitive in the new digital era.

According to the report “Machine learning in UK financial services”, published by the Bank of England and the Financial Conduct Authority, 52% of its survey respondents have a dedicated strategy for research, development, and deployment and 19% are establishing or already have a dedicated center of excellence that works to promote machine learning deployment across the organization.

Challenges and opportunities of AI

Despite the enthusiasm, however, implementations are not without challenges. Unsurprisingly, some of the main risks often cited by financial institutions include explainability and transparency, biases in data and algorithms, validation or governance, and model performance. Are humans kept in the loop and are guardrails in place? How do we validate that the models are performing as expected and that the outcomes are fair?

This is crucial as lenders are increasingly using alternate data sources to extend credit to an expanded pool of customers. And what about the quality of the data? As Veda Bawo, Director of Data Governance commented on a recent MIT Sloan Management article, “You can have all of the fancy tools, but if the data quality is not good, you’re nowhere.”

BOND.AI founder Uday Akkaraju and I recently co-authored a piece in the Harvard Business Review that discussed questions around data ownership, data governance, and ethics as data relationships become more complicated and as partnerships with between multiple entities proliferate and new business models emerge. Imagine when “access to credit is gauged not just by our credit history, but by the friends in our social media circle. When our worthiness is determined through an algorithm with little to no transparency or human recourse; when our eligibility for insurance is determined by machine learning systems based on our DNA and our perceived digital profiles.”

Whose values will the algorithm be based on then? Whose ethics will be embedded in the calculation? How do we preserve transparency and fairness – and ensure that we are working with a glass box, instead of a black box?

We are only at the very beginning of the AI journey. In the near future, we can expect to see improved personalization and new products and services, that will enable financial institutions to become true partners for their customers. The question isn’t “if” this will happen; rather, it’s “when”.

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Join Theo in this week’s new episode of “The Other 50” via iTunes and Spotify, where she talked with Joyeeta Das, CEO and Co-Founder of Gyana, about the promise and peril of artificial intelligence, and why it is important to dream.

See more articles by Theo here.

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