Artificial Intelligence could be the next big thing to radically change financial services. The industry is becoming increasingly aware of AI’s potentially pervasive applications in the business.

Financial institutions have already used AI and data analytics to assess clients’ credit risk and to process client information. Now, automation and the use of digital assistants in the industry is helping these institutions increase their productivity and service quality too.

Shameek Kundu, chief data officer at Standard Chartered Bank, believes that this is only the tip of the iceberg. Traditionally, banks have used data analytics to assess the risk profile of customers, Kundu explains, based on client and historical transactional data, as well as to determine what services to offer.

However, he also sees potential in having machines “read” paper documents and extract relevant information by optical character recognition with some degree of natural language processing, improving efficiency and reducing manual processes.

Robo-advisors for investment?

Some other areas where AI could have significant impact would include “robo-advisory”, where digital platforms provide investment advice, fraud detection and prevention, and customer service.

Robo-advisors can collect and collate information about the risk appetite and financial goals of an investor, and dish out investment advice accordingly. By lowering the costs, a much broader group of customers can potentially gain access to financial advice, a service which was previously the exclusive domain of private banking clients, Kundu notes.

Preventing financial crimes involves a huge amount of data. It’s like “looking for a needle in a haystack”, Kundu says, given the large amount of customer and transactional data that have to be processed to detect sanctions or money laundering risks for instance.

On the back of better data quality and more targeted and intelligent algorithms, banks may be able to cut through the noise of false alerts and obtain more targeted hits. This will allow for better follow-up investigations.

Helping financial consultants work smarter

Chatbots with conversational AI abilities will also change the landscape of customer service in the financial services industry. They reduce the manpower required to run call centres, freeing up precious human resources to perform more “value-added” tasks.

Prudential Singapore can attest to this. Its financial consultants have access to askPRU, a chatbot that can provide real-time information on customers’ life insurance plans, such as the policy premium due date and status of submitted claims. It responds to both text and voice queries.


“Thanks to AI, we are able to improve our service levels by freeing up capacity for our people to handle more complex issues that requires more judgment and decision-making.”

In the past, such information would have to be manually retrieved by calling the call centre. This has changed after askPRU was introduced. The chatbot answers more than 1,000 queries each day, helping to reduce call centre volume by more than 35%. “Thanks to AI, we are able to improve our service levels by freeing up capacity for our people to handle more complex issues that requires more judgment and decision-making,” explains Chief Information Technology Officer, Arvind Mathur.

Push for AI adoption

Governments across the world have also shown strong support for the promotion and adoption of AI across all sectors.

On this front, China is largely seen as leading the charge. It has set out a comprehensive roadmap for its plans to be the world’s leader in AI, and will be investing at least US$7 billion in AI.

Elsewhere, the United Kingdom has pledged US$30 million to build AI tech incubators, and has announced a deal between private and public groups that would bring more than US$200 million of AI investment into the country. France will also be investing US$1.8 billion in AI research till 2022, and in 2017, Canada launched the US$97 million PanCanadian Artificial Intelligence Strategy, which aims to attract and retain top AI talent, and will provide funding for AI research centres in the country.

In 2017, Singapore’s Infocomm Media Development Authority announced a national programme to boost Singapore’s AI capabilities, and that up to US$113 million will be invested in the programme over the following five years.

Specific to the financial services sector, the Monetary Authority of Singapore (MAS) put in place a US$20 million Artificial Intelligence and Data Analytics grant, to promote the adoption and integration of AI and data analytics in financial institutions.

Challenges and constraints

However, Standard Chartered Bank’s Kundu views the adoption of AI in the banking industry as in the “exploratory stage”.

Access to quality data will be a key hurdle to implementing AI, he says. Kundu also highlights that many financial institutions tend to be traditional, where data may not be seamlessly shared between different organisational groups, effectively restricting access to the proprietary data already owned by the financial institution.

Another constraint would be attracting and retaining talent necessary for implementing AI. While a dearth of talent is not a new issue confronting the industry, the problem is exacerbated by the fact that expertise in the AI field is relatively niche, although that is likely to change in the future.

To create greater awareness on the impact of AI on the industry, Singapore’s Ngee Ann Polytechnic has partnered with London-based Centre for Finance, Technology and Entrepreneurship to launch an online course on AI in Finance in June 2018. Through the course, which has an expected enrolment of 2,000, with local banks such as DBS, OCBC Bank and UOB showing keen interest and committing to substantial enrolment.

In addition, the financial services industry is highly regulated, and the use of customer data in financial institutions for AI applications will be subject to similar scrutiny. Given the unfamiliarity of the inherent risks to the business that AI introduces, business executives may be less willing to adopt relatively unknown technology.

To help navigate the uncharted waters of AI regulation, the MAS formed a committee of industry stakeholders to develop a guide on the responsible and ethical use of AI and data analytics by financial institutions.

The guide will “set out key principles and best practices for the use of AI and data analytics, helping financial institutions to strengthen internal governance and reduce risks of data misuse”.

As industry works to lower the barriers to adoption, it may one day be a best practice for myriad services to be based on algorithms – saving costs, time, and freeing up humans for higher-level work.