Perspectives

Future of banking

Sreekanth Lapala
Senior Vice President – Outsourcing Transformation Services
Article

In the present era of global, interconnected financial systems, a small ripple in one nook and corner of the world can probably create a shock wave in the entire economic ecosystem. This has led to increased scrutiny and more stringent and intricate regulatory requisites for the financial institutions. Regulators have not only shortened the time but have also doubled up the information they sought from their customers. Risk, financial data, and technology are now hot topics of discussion, and banks are now looking for viable solutions that can help them sail through this expedition of compliance. To become more agile and remain relevant, traditional banks in India are exploring their technological options with more focus on insights into customer behavior.

Data Analytics Helping Banks in Regulatory Compliance

The world’s top investment banks were fined close to $43 billion over the past few years resulting from not adhering to the compliance rules in customer reporting, thus making it the single most expensive compliance issue. The regulators continue to put more pressure on the financial services firms by adding constant reporting and more regulations such as KYC, Basel III, and Solvency II to maintain data compliance.

While attempts have been made to deploy big data tools to increase analytics, using analytics has been a painful endeavor for many banks. Banks are still behind other industries in implementing new analytics techniques, which is why they face many data challenges today like data overload, inadequate data management systems, increasing customer demand for information, and increased regulatory reporting.

As banks continue to become more diverse and complex, technology radically changes the speed of operations and data production. Model banks need to apply big data processing and reporting tools to ingest and process data from both new and legacy sources to ensure compliance. These platforms should be sophisticated enough to process all types of structured and unstructured information such as internal documentation, voice-mails, customer correspondence, transactions, and the like. There should be a rich process modeling capability that can be used to detect patterns based on pre-defined regulatory reports to identify the risks quickly.

Establishing this type of big data analytics platform allows banks to reduce the complexity of their process and improve the speed of their analytical cycles. This allows banks to not only lower the cost of data processing but to also discover new insights by identifying and managing risks more proactively. By harnessing big data analytics for both compliance and improvements in core operations, banks can leverage and spend efficiency across their business lines and seek improvement in areas such as customer and fraud analytics.

Transforming Regulatory Compliance Through Artificial Intelligence

New innovations in data analytics empower banks with systems that are smart in automatically refining their algorithms and improving their results over time. We are not talking about the old school approach to data analysis — spreadsheets, data tables, and crunching numbers on a calculator. We are talking about artificial intelligence (AI).

Advances in automation and data-led intelligence has put sophisticated AI technologies within reach of traditional institutions. This is because the modern AI platform can stand on the shoulders of the data and process automation technology trends that precede it. AI is a collection of technologies such as Natural Language Processing (NLP) and machine learning that is being applied across banks to automate the processing of information and better interpret and contextualize the information. It has the potential to substantially refurbish the whole compliance process that is operational at a bank.

NLP is well suited for processing financial documents to extract metadata, identify entities, and understand the intent or purpose of documents. NLP can be used to identify the types of products such as loans or swaps and correlate it to a regulatory topic such as anti-money laundering, insider trading, or other abuse. When combined with robotics, AI simplifies the processes and reduces the chances of human error. As it will continuously self-improve, there are more chances of technology being able to manage complicated and time-consuming data updates better than its human users could ever do.

This means that banks are better equipped to deal with the demands of regulators in a manner that wouldn’t have been possible via any basic analytics tool or human intervention. Most AI systems are not at that stage yet, but the potential for transformation is enormous.

Improving the Customer Experience Through Big data

Banks have access to more consumer data than other businesses. With frequent use of web and mobile banking channels, the volume and variety of data that banks hold about their customers has steadily increased, driving an increase in the number of customer interactions. Banks hold detailed customer profiles, information on spending and income, and a clear picture of where people spend their time, banks are in a unique position to paint a clear picture of each of their customers.

Big Data offers banks an opportunity to differentiate themselves from the competition. Using advanced big data techniques to collect, process, and analyze information, banks can provide better personalization and relevant information to customers across all areas of retail banking. This can ultimately make banks more customer-centric. By using customer data effectively, banks can deliver more targeted and cost-effective marketing campaigns, design products, and share offers that are tailored to customer’s specific needs. Combining data sets in creative ways can surprise and delight customers, leading to retention, loyalty, and a higher lifetime value.

Big data can also play an important role in customer retention by minimizing churn. Loyalty has become a top issue with the millennial generation. It costs banks significantly more to acquire new customers than retain existing ones, and it costs far more to re-acquire deflected customers.

All this shows that it may be a tedious journey for banks to deploy these technologies now but will render great results in the future. Capturing these opportunities will require investment, planning, and coordinated decision-making throughout the institution. Automation is rewriting the rules of how banks compete. Banks that fail to grasp this risk may damage the franchises built over generations. But if they manage to address these multiple strategic challenges, they can position their institutions to compete effectively and capture an emerging, long-term growth trajectory.

This article was originally published in Business Standard.