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 becoming hot topic of discussions, and banks are now looking for crucial and 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 behaviour.
Data analytics helping banks in regulatory compliance
The world’s top investment banks have been fined with close to $43 Billion over the past few years due to not adhering to the compliance rules in areas such as customer reporting, and 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 such as Hadoop to increase analytics, in the size, complexity, and regulatory compliance but using analytics has been a painful endeavour for many banks. Banks are still behind other industries in deploying 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. To ensure compliance, model banks need to apply big data processing and reporting tools to ingest and process data from both new and legacy sources. These platforms should be sophisticated enough to process all types of structured and unstructured information such as internal documentation, voice-mails, customer correspondence, transactions, etc. There should be a rich process modelling capability that can be used to detect patterns based on pre-defined regulatory reports to quickly identify the risks.
Establishing this type of big data analytic 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 enables them to 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 though 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 the new age transforming access i.e. artificial intelligence (AI).
Advances in automation and data-led intelligence has put sophisticated AI technologies within the reach of traditional institutions. This is because the modern AI platform can essentially 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 now being applied across banks to further automate processing of information to 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 the 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 can hugely simplify the processes and reduce 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 undoubtedly means that banks are better equipped to deal with the demands of regulators in a manner that just wouldn’t have been possible via any basic analytics tool or human intervention. Most AI systems are not at that stage just yet, but the potential for transformation is huge.
Improving 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 analyse 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 offers that are specifically tailored to customer 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 some great results in the future course of time. Capturing these opportunities will require investment, painstaking planning, and coordinated decision-making, spanning the whole bank. 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.