The Fintech revolution is threatening to completely disrupt conventional banking models. But for most Fintech startups, access to consumer data is still a huge challenge. In contrast, the incumbents have a wealth of transactional and behavioral data at their disposal. In the digital paradigm, the flow of data will only increase as each bank accumulates terabytes of data from a variety of external data sources.
Advanced analytics technologies can help banks leverage the abundance of data at their disposal to gain granular, real-time insights into every aspect of banking operations. These technologies empower banks to define their customers, based on their individual values, expectations, and needs, rather than aggregated demographics.
Take online and social platforms as an example. As more and more customers turn to these online communities to discuss their opinions and preferences, there is a huge opportunity for banks to turn these interactions into significant business value. For instance, sentiment analysis of social commentaries allows them to drive continuous product and service improvements. It also enables them to generate richer insights into individual customers’ life stage circumstances and personalize the banking experience to the ‘segment-of-one’. This means that banks can now personalize banking experience based on every bit of customer data, including a new addition in the family or the stated intent to purchase a new car. Big data product-matching algorithms can then help deliver products that are aligned with customer preferences, thus significantly increasing the probability of success. Banks can also leverage sentiment analysis to map customer perceptions about the competition, and use those insights to design more targeted and productive acquisition strategies.
Advanced analytics technologies have a host of significant applications across the banking value chain. Currently, the analytics conversation is still dominated by its potential for risk, fraud, and compliance management. And this is rightly so, considering that the industry loses billions to fraud, every year. Compliance norms are also getting tougher, with regulators imposing huge fines on some major banks for KYC and AML violations.
The proliferation of digital touchpoints and explosion in digital transactions is only going to exacerbate the situation. More importantly, the increasing complexity of reporting norms, amidst a zero-tolerance approach to non-compliance, will make analytics a practically indispensable part of risk and compliance management as traditional detection and control systems struggle to cope with the rigors of the digital banking business.
Advanced analytics technologies can help compliance officers take a more unified, enterprise-centric view of risk, by connecting the dots across multiple risk and compliance layers within the organization. They also enable a more proactive approach to the management and mitigation of risk by extracting actionable intelligence from a range of structured and unstructured data sources. Advanced analytics tools can map financial patterns across accounts and channels in real-time, to flag suspicious activities and behaviors that deviate from the norm.
That being said, advanced analytics can drive opportunity and value in almost every banking function. Take the technology ecosystem for example. Using advanced IT operations analytics platforms, banks can now monitor and analyze application and infrastructure usage alongside performance issues across the technology landscape. Banks can enhance the returns on expensive infrastructure investments by taking an insights-based strategy to optimize capacity based on current and expected usage. A data-driven approach to technology and application management will also allow them to strengthen business outcomes and innovations.
In the era of the ‘segment-of-one’, banks can leverage big data analytics to drive tremendous contextual value throughout the customer life cycle. By looking beyond traditional structured data sets, banks can now generate more targeted customer acquisition leads. A customer’s product usage patterns can inform product development strategies and even uncover potential opportunities for up- and cross-selling. Analytics also allows banks to enhance the ROI of their marketing programs by eliminating intrusive, irrelevant, and unproductive offers.
In a time of fickle loyalties, analytics and predictive modeling techniques allow banks to constantly monitor customer satisfaction levels and predict attrition before it happens. That apart, analytics solutions can even help match acceptability and profitability across a range of possible retention offers.
As more and more customers turn to digital channels for their routine banking interactions, it is imperative to enlist every banking employee in the battle for loyalty and wallet share. This means empowering every role, however junior, with the power of analytics. When empowering employees – or indeed, any stakeholder – with analytics, the focus must be on adopting easy-to-use visualization and prediction technologies that enable them to quickly convert complex data patterns into actionable intelligence. In order to fully integrate the potential of analytics and to ensure that all employees, partners, and customers are ‘analytics-enabled’ as required, truly digital banks will increasingly turn to cloud-based, open-source technologies in the coming year.
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