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Artificial Intelligence in Financial Services: Opportunities and Challenges

By: Dr. Amit Saraswat, VP, Data Science, Lendingkart Group

Artificial Intelligence (AI) is by no means a fresh concept. When you consider it as the ability of machines to interact and learn to perform faster tasks that were previously performed by humans, it has been around for over six decades, lurking in scientific analysis, big data and targeted personalized customer treatment. But, as an idea, Artificial Intelligence is still a novel one; it is still the focus of numerous dystopic science fiction novels and still excitedly whispered about in gatherings of great scientific minds. This could possibly be due to the wide potential it offers and mostly because we, as a species, are yet to grasp the countless possibilities that it promises.

For obvious reasons, machine learning improves over time as more data is processed and as more positive results achieved. Owing to this ability, AI has surpassed our expectations so much that Ray Kurzweil, computer scientist, inventor and futurist estimates that it will exceed human intelligence by 2019. Heightened interest in AI has occurred because of both capabilities and business needs. The explosive growth of structured and unstructured data, availability of modern technologies such as cloud computing and machine learning algorithms, rising pressures brought by new competition, increased regulation and heightened consumer expectations have created a ‘perfect storm’ for the expanded use of artificial intelligence in financial services. Despite AI proving its worth in virtually all industries, the banking and financial sector seems to have taken a more cautious approach.

Having said that, in recent years the financial sector has invested tremendously towards developing and implementing AI for speed, accuracy, and efficiency ensuring proficient functioning of various systems. Titans of the wealth management industry, such as Barclays and Charles Schwab are already implementing AI into their operational infrastructure. By applying human-centered design thinking to AI practices and designing AI for humans, Barclays hopes to use artificial intelligence to deliver what each customer wants but the scope of AI doesn’t just stop with customer experience. Wall Street trading pits have been entirely transformed, with most trading now performed by AI computers. According to a report by Goldman Sachs, AI trading software can collect enormous amounts of data every second and make new learnings and predictions about stocks, bonds and commodities at a pace that can outmatch several humans. Moreover, there have been instances of AI being used as a virtual assistance. For instance, DBS Bank’s Digibank app uses AI for virtual assistance via text and speech to respond to user queries. All the while, AI can continue to ingest new data to keep up with global trends, right up to the millisecond, always learning and improving on conclusions and predictions. A report from Eurekahedge monitored 23 hedge funds that function on AI and found that they, by a large bracket outperformed funds overseen by people.

While banking is an age-old industry that is weighed down by monolithic systems, rigid regulations, and cultural traditions, change is coming. Owing to advancements in automation and data-led intelligence, financial AI technologies with minimal, day-to-day impact on workflows are becoming feasible while still maintaining compliance with existing or emerging regulations. This is because knowledge repositories that capture boundaries and basic interaction rules — regulatory protocols that need to be digitized if new AI systems are to remain within the boundaries of the law — already exist. AI, in essence, stands on the shoulders of the data and process automation technology trends that preceded it. These trends, combined with new machine learning technologies, will allow financial services providers to concentrate on high-value activities and creative solutions. Automated systems will be able to handle volume-based and repetitive activities at lower costs, enabling higher productivity and reducing the need for oversight — all the while ensuring that banks can deliver compliant sales and service outcomes.

Financial services organizations today use AI-powered solutions for several primary functions including personalized communication with customers, identifying data patterns that would otherwise have been missed and increasing workforce productivity. Additionally, the increased use of AI solutions helps give a company an edge over its peers while continuing to be a fresh and innovative workplace.

Considering the future, while we realize that AI can prove to be invaluable to the growth of the fin-tech sector, the machines won’t take over just yet. While they may replace humans in some areas of functioning and play the role of personal assistants and digital laborers, there are challenges like bias, privacy, trust, trained staff and regulatory concerns that continue to be a hurdle to be dealt with. Augmented Intelligence, in which machines assist humans in their system functions, could be the more plausible answer. Another key area where AI will continue to play a pivotal role will be in big data. Sifting through and analyzing thousands of pages of data is a burden and a waste of human resource and more and more of these machines will be used to perform advanced analytics of patterns and trends. ‘Robo-advice’ is another upcoming technological innovation that will help assist customers in making effective financial decisions. Getting closer to the customer through effective personalization will be one of the factor determining the impact of AI. Evaluating customer sentiments through pattern learning can play a critical role in the kind of services financial companies provides.

While there may be both success stories and tragic failures related to AI over the course of the next few years, for any first-generation technology company this will serve as a learning curve to adapt to and understands the uses of artificial intelligence while optimizing the data accordingly to provide quality customer service and boost company growth.

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