The human race is staring at a massive data explosion. By 2020, about 1.7 megabytes of new information will be created every second for every human being on the planet, according to estimates. The scenario as of now, however, is no different as many industries including the banking and financial services continue to pile up high-volume, high-velocity information assets known as Big Data. And on an average, organizations use only a fraction of the data they collect and store. The challenge is to decode such capacious raw data sets into strategic and effective insights, improving business prospects. Here, data analytics helps industries and organizations to make more-informed business decisions, using specialized systems and software.
In India, with the Government putting more thrust on financial inclusion as well as the adoption of modern methods like mobile banking and online payments, data analytics has become imperative to increase revenue, enhance customer experience, optimize cost structures and manage enterprise risks. Since adopting technology in 2013-14, the Indian financial services sector has seen a proliferation of data sources and technology platforms, challenging the ability of organizations to do justice to customer data. Thus, the adoption of data analytics is key to make banking more convenient, equitable and personalized to user needs. And as we strive to deepen financial inclusion goals, adoption of superior technologies and tools like data analytics would play a major role in managing risks, improving operations and cutting costs.
Let’s look at the benefits of technology and analytics in the grander financial inclusion objective.
Greater risk management: At present, banking technologies deal with three modes of risks; model and systemic, cyber security and contagion. Often, operational or systemic risks (within the ambit of an organization) lead to losses from inadequate or failed internal processes, people, and systems or from external events (including legal risks). Organizations must have a dependable insight into risks they are managing as well as the effectiveness of controls they have in place. Basel II mandates a focus on operational risks, seeking to identify, measure, evaluate, control and manage risks. Thus, the sector needs a sound operational risk management (ORM) practice. As the threat of new and unfamiliar risks looms, risk-management functions will need a futuristic perspective besides a systemic flexibility to detect and mitigate them. Data analytics can help them identify issues in real time and recalculate risk portfolios within a short period of time.
Customized communication: Banking sector is by default information intensive hence engaging a customer in real-time is key to retention. For example, mobile applications have brought customers closer, improving the quality of overall banking service. Though most of the banks have invested in technology and communication tools, data analytics can sharpen both the message and delivery, applying greater and more contemporary customer insights. It consolidates multiple communication modes into a single and more targeted mean to cut costs and complexities. For example, technologies like chatbots deliver simple tasks real time, allowing relationship managers to focus on more personalized interactions. Banks which are investing in innovative technologies, seeking innovation and disruption, can have their ‘Uber moment’ only if the communication is optimized and delivered through the right mode.
Personalized service offerings: An increasingly competitive environment, as well as a dynamic demography, are forcing financial services players to rethink customer-centric strategies. Banks, for example, are trying to create more and more commoditized products and solutions, keeping the price as a prime differentiator. However, holistic innovations at the customer level can come from smarter solutions which not only help banks acquire new clients or increase the revenue per customer but also facilitate cross-selling activities, thereby lowering operating costs. Such solutions should be platform agnostic; mobile solutions, for example, now suit both urban and rural areas thereby delivering financial inclusion at a lower cost. Similarly, electronic payments are becoming popular among customers who may now favor a bank that deals far more efficiently through digital means.
Analytics across a customer’s lifecycle: The banking and financial services sector has come a long way from storing basic customer details to creating and analyzing an individual’s progression in the financial space itself. From the prospecting’ and pre-approvals stage (beginning of a customer’s lifecycle), the data journeys to identify usage of services, retention needs, pre-delinquency till charge-off or collection recovery and fraud prospects, creating opportunities and learnings galore for institutions. But with the Internet and mobile devices adding real-time data to the customer behavior, the digital footprint of an individual has dramatically gone up, demanding more powerful computing tools to analyze and interpret the same. Predictability of a customer’s behavior lies in analyzing his lifecycle; from a mere account opening to applying for credit, it all adds up. The credit score, for example, gets developed in the process, helping institutions to accept or reject a loan request or it can convey if a customer is ripe for insurance of any kind. The data crunching – using alternative info such as utility bills, mobile phone bills and use of credit cards – can further refine the so-called thin-file clients’ into potential targets for various services.
Customer profiling: As a traditional marketing tool, customer profiling has gained much relevance today in the area of risk policy thanks to a more cutting-edge technological up gradation. Higher quantum of data, greater sophistication of analytics and the availability of multiple channels have sharpened the results, helping banks to profile customers from not only a risk perspective but through an opportunity perspective as well. Big Data tools and techniques help banks detect prospects of fraudulent activities by highlighting the exact challenges, breaking down a customer’s exposure to each product and service, he uses. Key details such as revenue per customer, email responsiveness, product mix and the purchase channels used are examined before segmenting each customer into groups, pegged in terms of risks as well as opportunities. Further, a rich profile of each customer is created to perfect the campaign messaging, channels and specifics (preferred time of the day, etc.).
Market Insights: Market insights from data analytics can significantly improve an organization’s basic capability to define, revamp and change their business strategy, using an overview of the market, portfolio quality, sourcing quality, segment specific reports and geographical trends. The depth of the analysis can be as profound as possible as for example, the portfolio quality reports can delve deep into the vintage composition and curves, delinquency reports and collection flow reports, etc., enabling institutions to refine service standards and strategies to be deployed.
By 2020, the median age of Indians will be 29 years – an ultra-young population with access to high-speed Internet and real-time services. They will inspire an explosion in the customer base, innovation in products and services and the introduction of newer platforms – which will require massive investments on behalf of banks and financial services companies. Here, Big Data analytics can transform the way financial services are designed; data mining can customize financial services from a perspective of utility and suitability besides targeting right customers with a more appropriate communication.
Try deep learning using MATLAB