How is Artificial Intelligence redefining the financial services landscape

Increasingly, financial organisations are adopting a machine learning-based approach to augment their algorithmic rules-based approach towards surveillance and risk management

Published: 08, May 2018

The author is the Group Vice-President, at Sapient Consulting

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Image: Shutterstock

Picture this: US-based banking and financial services major Wells Fargo started piloting an AI-driven Facebook chatbot early last year. It responds to queries from customers, like the current balance in their accounts, and even helps them locate the nearest bank ATM – all through Facebook Messenger. That’s just one example. AI has emerged as a powerful disruptor in the Financial Services industry. And most players have already hopped on to the AI bandwagon. In another couple of years, widespread adoption of cognitive systems and AI is expected to boost worldwide revenues. An IDC report predicts that the revenue growth could touch $47 billion in 2020.

Some of the key areas where most AI investments are expected to happen over the next few years are:

  1. A relentless focus on customer centricity – acquiring and retaining customers, providing innovative products and a better, holistic experience to customers
  2. Improvement in operations, cost management and focus on profitability
  3. Risk management, surveillance and fraud detection
  4. Exploring new ways of generating alpha
  5. Underwriting and claims management that are more specific to the insurance sector


Let’s explore these in detail.

AI helps drive customer centricity
Most organisations in the financial services industry are attempting to leverage AI to create powerful and well-connected customer journeys. For instance, as a customer steps into a bank branch, its AI-enabled facial recognition system identifies the person in just a few seconds. It reveals more connected information like the customer’s spending patterns and recent financial transactions. AI helps the bank discover that the customer has been traveling frequently to Europe on business, and spends generously on his credit card. The bank uses all this relevant information to recommend its new, multi-currency card to the customer.

Several banks are already using AI to drive customer centricity with the help of automated and chatbot-driven personalized interactions. Hyper-personalization is the core idea behind customer centricity. Each individual’s needs are unique and different from everyone else’s. Creating (or customizing) products for each customer is the ultimate holy grail.

Improved operations, efficient cost management and focus on profitability
Today, banks are facing significant pressure on their margins. Regulators and their relentless focus on transparency have rendered several businesses, unprofitable. While financial services firms continue to look for new customers and new products, it is absolutely imperative that they manage their bottom lines. This is where AI comes in. AI technologies are enabling them to bring more efficiency to their operations and cost-management. The dramatic rise of technologies like Robotic Process Automation (RPA) and Intelligent Process Automation (IPA) is further fueling this.

Parsing financial deals that once kept lawyers at JPMorgan Chase & Co. occupied for thousands of hours is just a matter of a few seconds today. Making this possible are a contract management software and a learning machine. Another case in point: Machine learning algorithms help Morgan Stanley augment its 16,000 financial advisers. These human brokers can suggest trades to the customers and take over routine tasks at the organisation.

The rise of RPA technology (actually, Intelligent Process Automation is the next significant leap) is becoming a huge enabler in terms of bringing efficiency and improving productivity.

Risk management and fraud detection: The AI assurance
The recent Punjab National Bank scam and other similar frauds have sent shockwaves across regulators, financial and stock markets, and the banking industry. These multi-dimensional frauds involving crores of rupees have brought into sharp focus the need to have in place a fool-proof surveillance and fraud detection system. Coupled with stringent KYC needs and money laundering regulations, it is becoming absolutely essential for financial services firms to pay utmost attention to this area.

Financial services providers are inundated with big data, especially unstructured data, starting with phone calls of traders to emails of dealers. However, the approach to surveillance has been very people-centric, through audits and sampling. However, lately, with the rise of AI-based systems, it is possible to analyse volumes of business data and find out how well the internal control systems are operating. Increasingly, financial organisations are adopting a machine learning-based approach to augment their algorithmic rules-based approach towards surveillance and risk management. Machine learning techniques which are constantly learning on the job, can keep a few steps ahead of human- and rules-based fraud detection systems. AI and machine learning are proving to be game-changers in detecting insider trading to market abuse.

Generating alpha
In the trading and investment management business, finding alpha or new ways of making money is the holy grail. Over time, regulatory oversights, standardization of products (like exchange traded derivatives) and transparency requirements have made finding alpha increasingly difficult. This is where new ideas, especially AI-driven ideas are becoming more and more interesting. This is where platforms like Kensho and other machine learning platforms are processing tons of historical data to find correlations human minds could have never identified. Not just correlations, what is intriguing is that AI or machine learning technologies can look at an unfolding event and predict how the markets may move, based on historical analysis of similar events. This is giving concrete actionable items for investors to work against. Similarly, news and sentiment analysis is another area where machine learning is playing a big role in identifying investment opportunities.

AI in the area of insurance underwriting and claims
Most players in the Insurance sector are reaping the rewards of the recent advancements in AI, that have helped them solve complex challenges in the areas of underwriting, claim handling, and fraud detection, among others. Insurers are constantly looking for data that can help them understand customer behavior in a better way. Anything that can help them identify risky behavior will allow them to charge higher premium for high-risk individuals and offer lower prices to the others, thereby reducing price on an average. In UK, an auto insurance firm is offering lower rates if the driver is willing to install a small device in the car, which will monitor his driving habits. As insurance firms collect humongous amounts of data, their mathematical models are improving significantly. They can predict risky behaviours with far more accuracy, and hence target the right individuals. Very soon, I believe, health insurance companies may offer lower insurance to fitter customers willing to share their Fitbit data with their insurers.

One word of caution across all these examples: as data becomes more important for the effective working of these machine learning or artificial intelligence models, so does the privacy and governance around the data. Who can access data and for what purpose need to be controlled and all financial services firms will have to up their game significantly in this area. There is a very thin line between trying to help customers and infringing on their privacy.

The impact of AI and the way ahead
Financial services players continue to embrace AI. But are there enough skilled people to meet the rising demand? True, not everyone who works on AI needs to be a seasoned data scientist. But there’s no disputing the fact that AI still remains a niche domain. Although an understanding of AI and machine learning is now more commonplace than ever before, there’s a shortage of relevant talent in the market.

The need of the hour for financial institutions is to focus on talent. It is also imperative that financial services, especially in India, bring together cross-functional teams, possessing sound knowledge of the financial business, who can focus on building some of the key use cases. As the organisation’s knowledge and capability improves, it would be easier to ramp-up their investment in the coming days

Given the recent incidents of frauds, financial players should pay more attention to the security and privacy of customer data. Never before had the industry felt such a pressing need for effective governance and compliance.

As investments in AI increase, it will bring a new phase of creativity and vitality in the finance industry, resulting in newer opportunities for everyone.

-The author is the Group Vice-President, at Sapient Consulting

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