The world is witnessing a steady acceleration of digital transformation across industries. The banking, financial services and insurance(BFSI) sector is no exception. Today, data is the new oil and Artificial Intelligence (AI) has emerged as the best means to harness its potential. The demand for AI is rising as organizations strive to stay relevant in the 21st century. According to Allied Market Research, the global market for AI in the BFSI sector is expected to reach USD 247 billion in 2026, up from USD 17.8 billion in 2018.
AI has significant benefits for financial institutions. It can quickly and seamlessly analyse huge volumes of data, allowing them to automate previously manual processes, enhance customer service and effectively combat fraud. It also reduces costs by enhancing operational efficiency and provides new ways to unlock insights from raw data. As insights from data science labs make their way to everyday processes, AI is changing the way we bank forever.
Enhancing customer experience
Today’s customer seeks quick and personalised solutions. In recent times, conversational AI-driven virtual assistants have emerged as a popular and useful solution to this need. Virtual assistants are accessible 24×7 on multiple channels, from messenger apps to email. They provide personalised and instant responses to a range of requests, from finding the nearest ATM and balance checks to credit card bill reminders.
Virtual assistants respond to thousands of customers every month, improving the reachability of BFSI companies. In other cases, AI supplements the services provided by banking call centres, which have employees specialised in different areas of the business. AI can route queries and requests to the right employees to help customers get the answers they need. Further, it can also analyse a customer’s spending patterns and create unique offers that encourage them to redeem loyalty points. Customer satisfaction leads to greater loyalty and even increased referrals.
Protecting against fraud
The past decade has witnessed a fundamental shift in the way payments are conducted. Cash, cheques and credit cards are gradually giving way to digital modes of payment. Simultaneously, fraudsters have developed more sophisticated methods to steal that are difficult to track using the static, rule-based systems that BFSI companies have traditionally used.
Today, AI and Machine Learning provide BFSI companies with the means to combat this issue more effectively. They offer greater flexibility since they are self-learning and quickly adapt to new types of fraud . Thus, they can process enormous volumes of data – including customer history, fund flows, transaction history, and behaviour analytics – to detect unusual patterns with precision within little time. They can, therefore, detect activities ranging from fraudulent transactions and identity theft to loan scams and email phishing, thereby saving millions of dollars.
Automating for greater efficiency
Several functions – such as customer onboarding, regulatory compliance, accounts payable, Know Your Customer (KYC), account closures and customer service – no longer require human intervention. Tasks that would take hours or days manually can be accomplished in seconds by AI. Automating these tasks can eliminate redundancy and inefficiency. Automating core decision points removes the risk of individual bias and helps organisations make consistent and fair decisions. By reducing human error, automation helps them reduce losses. It streamlines workflows and enhances efficiency. A report by Business Insider recently indicated that the aggregate potential cost savings for banks from AI could reach $447 billion in the next two years. Human effort can be better utilised in more critical tasks.
Filling critical gaps through process mining
Process mining refers to techniques that draw on data science and process management to improve business operations. It allows businesses to extract actionable insights from event logs. Process mining has become a critical part of data science toolkits used by financial organisations. Over the last several years, the digital footprint of customers has increased tremendously, along with the interactions among different systems in an organisation. As processes become more complex, so does the data generated by these interactions. Process mining helps organisations visualise data flows to determine critical inefficiencies and unknown risks by helping them understand their processes and interactions better. The insights achieved through process mining can help organisations enhance their customer experience, eliminate risks and improve their operations.
Unlocking value of dark data
Dark data refers to non-transactional data that is captured and stored during regular business hours but whose value is difficult to unlock. It could be present in semi-structured assets – such as contracts and invoices – that cannot be accessed through traditional search means, or through unstructured assets like emails, videos and logs containing information that organisations would like to access but are not sure if it exists. It can provide insights such as the frequency of website visits, customer purchase history and their geographical spread. In short, dark data is data “that organisations know they have but find it difficult to access or data they do not know if they have but would like to access”. Currently, new AI-driven models are being developed to make connections between seemingly unrelated pieces of information. It can help organisations find new ways to enhance their services and develop a competitive edge.
Improved customer satisfaction, enhanced operational efficiency and greater data security are taking centre-stage as the world goes increasingly digital. Today, AI provides banks with the means to achieve all of this and the ability to ride the changing tides of the 21st century.
The author is CTO- Financial Service APAC, Hitachi Vantara