Artificial Intelligence (AI) is spurring the next wave of the digital revolution. As we enter the cognitive age, forward-looking financial institutions, especially banks, are leveraging AI to transform every aspect of their business—from customer engagement and R&D to cybersecurity and back-office operations. For these firms, AI is more than technology; it’s a new way of doing business and galvanising strategic, operational, and financial performances.

In the Indian Context

AI will continue to improve the manual and repetitive processes that underpin much of financial services. In the backdrop of a wide range of emerging AI/ML use cases, almost all financial services are expected to be automated in the future, especially with the increasing rise of digital-only transactions. Banks can thrive by scaling initial use cases to industrial scale and handling its effects on the organisation. However, managing via algorithms as opposed to processes will be a considerable challenge. Customer journeys, staff responsibilities, oversight regulations and operational processes are going to change alongside the technologies.

In its 2020 year-end “Report on Trend and Progress of Banking in India”, the Reserve Bank of India has highlighted resulting effects of the pandemic on various Banking and Non-Banking institutions. Growing verticals like lending, investments and digital payments are witnessing the entry of FinTech start-ups, digital currencies, and the like. Therefore, traditional solutions for regulated entities may not be enough for risk and compliance management.

Much like other sectors, digital transformation has been hastened by the confluence of factors like the pandemic and advances in the AI/ML techniques. Based on responses from over 1000 banking executives globally, a recent Global Banking Benchmark Study by Publicis Sapient found that both incumbents and challengers in the Banking sector cite customer experience and operational transformation as two major pillars of digital transformation. Let’s explore how AI/ML capabilities can help these institutions run the wheels of transformation and the steps that they need to undertake while implementing these technologies.

Impact on Financial Services

AI is perhaps more important in the long term than in the short term. A few ways AI can profoundly impact FS institutions in the future are:

Operational process automation: Banks are automating core operational processes through AI and ML. Redesigns that incorporate robotic process automation can explore improvement opportunities, leading to an exponential rise in the growth of automation.

Customer acquisition and retention: Armed with third party data, banks are leveraging marketing technology (Martech) to find new customers and improve their experiences with customized interfaces based on their preferences. AI enabled customer-facing services allow document uploads and identity verification with just a smart phone camera, vastly improving the customer experience. It allows the tracking of customer engagement with the bank throughout this lifecycle, and then using all of this information to drive the right interactions with that customer with the right channel.

Workforce management: With customers exploring new financial services, banks must field their requests most efficiently and productively. This requires a more intelligent AI-based triage system that prioritises requests in terms of significance, and then directs them to chatbots, online forms or call-centres, which cannot handle every request.

Credit risk: New technologies are improving models for delivering credit scores. Cloud infrastructure enables the processing of massive, hitherto-unavailable datasets and testing new models at unprecedented speed. With improved methodologies and testing capabilities, researchers can trial different AI and ML models until they deliver precise credit scores for individuals.

Regulatory compliance: Banks undergoing major changes will need to comply with emerging laws around digital innovation. Regulatory agencies understand the necessity for new safeguards and rules to be reshaped by AI and ML. New approaches can change the risk profile of banks for better or worse.

Cybercrime prevention: Based on common criteria, AI algorithms can identify money-laundering attempts with a high degree of accuracy. For instance, MasterCard was able to reduce fraud by 50 per cent this way. Banks can play a proactive role in protecting the public during these strange times.

Action plan to drive transformation: Financial institutions and regulatory authorities are increasingly becoming innovators rather than being adopters of AI/data techniques. Here are four steps financial institutions can take while digitally transforming to optimize the use of AI/ML:

1. Clarify objectives, identity use cases

Any good data strategy needs to first define goals and identify smaller use cases that can help them achieve them in the long run. Maintaining an idea repository and identifying the most promising ideas through rapid proof-of-concepts and minimal viable products (MVPs) are some good examples of why we should think of AI/ML tracks of work as agile experiments. Banks should consider a venture capital-style funding mechanism for trials before scaling solutions.

2. Use the right data effectively

Identifying and using the best data for analytics and AI/ML continues to be a huge challenge for most firms. The next hurdle is to ensure proper governance. This a structure with the right scalable platform, people and processes to support the use of data.

3. Hire the right talent and capability

A pre-requisite for digital transformation is to recruit the right talent (both technical skills and culture) as well as upskilling existing technology and business users.

4. Build the right partnerships

Identifying specialists who can help banks in the digital transformation journey is paramount. This could be established through product companies, fintech start-ups and capability consulting firms, who will provide invaluable insights on competitor landscape and evolving best-practices.

Financial institutions must know what they can deploy now and understand future challenges. This will allow them to work toward redesigning their businesses.

The author is VP Engineering, Publicis Sapient

DISCLAIMER: Views expressed are the author's own, and Outlook Money does not necessarily subscribe to them. Outlook Money shall not be responsible for any damage caused to any person/organisation directly or indirectly.