6 Ways Banks can Offer Better Services to the Digital Customer

By Venkatesh Vaidyanathan, VP, Product Management, Infosys Finacle

6 Ways Banks can Offer Better Services to the Digital Customer

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Customers demand digital banking because the conventional banking model does not deliver the experience they have become accustomed to from other services industries like retail.

Banking has always been a data-driven technology-centric business. So, progressive banks globally understand the promise of big data analytics. They endorse its potential to transform business aspects like risk management, customer engagement and operations. Most banks already use these technologies in areas like customer segmentation or fraud management.

Here are 6 ways banks can effectively cater to the digital customer:

Personalizing for the segment of one: Eight percent of Asian banking customers are willing to change financial service providers in favor of a better proposition. At the same time, 70 percent of all global banking customers are willing to provide more information if it leads to greater personalization.

Banks need to leverage every customer data from a new addition to the family or the need for a new car when personalizing the banking experience. Advanced analytics can combine customer data from various sources to help banks segment customers based on their values, expectations and needs, rather than by broad demographics.

Big data product-matching algorithms can then help deliver products that are aligned with customer preferences, significantly increasing the probability of success.

As more and more customers take to online and social platforms to air their opinions, banks can leverage these unstructured informal data sources to extract significant business value.

There is a huge opportunity to drive continuous product and service improvements based on customers’ stated sentiments. Sentiment analysis can also help banks structure loyalty programs on the basis of topical moods among customers. Banks can even use it to build a better understanding of the competition’s customers and develop more targeted, productive acquisition strategies.

Managing loyalty and attrition: In most banks, attrition is detected only when the customer issues a notice for termination of services. But most customer departures are the result of a sequence of escalations that lead to the proverbial last straw. Conventional customer management systems are simply not equipped to track detailed relationships.

But with the complex event processing capabilities of big data, banks will be able to track the escalation in real-time. That said an early warning system is only one half of the solution for successful customer retention. The other is the creation of a mutually acceptable retention offer. Here again, analytics can help match acceptability and profitability across a range of possible retention offers and even recommend the ideal channel of delivery.

Streamlining marketing and campaign management: Most marketing programs in banking take a shotgun approach of delivering intrusive, irrelevant offers to customers, wasting precious bank resources along the way. But a US bank was able to achieve a mammoth 600 percent increase in marketing ROI as well as a 20 percent cost reduction by leveraging the power of analytics.

Analytics can transform the marketing function by enabling banks to deliver offerings that are tailored to a specific customer’s financial needs. It also makes it possible to determine pricing based on the likely future value of the customer. Using cross-channel analytics, banks can even identify the customer’s preferred channel for receiving the offer. Event-trigger engines can proactively alert banks to marketing opportunities as and when they occur.

Empowering employees with analytics: Customers are increasingly turning to digital channels for their routine banking interactions. It is, therefore, imperative for banks to enlist every employee in the battle for wallet share. The first step to realizing this strategy is to ensure that all staff members, including call center agents, are analytics enabled.

A leading Indian private bank has done this successfully to increase sales productivity by a factor of five. Once every employee has access to detailed transactional and behavioral information about customers, every interaction becomes an opportunity to cross- and up-sell products.

In order to successfully embed analytics as a culture, banks need to deploy easy-to-use predictive tools that are integrated with existing business intelligence and reporting systems. Visualization tools and technologies will also play a critical role in the process of ensuring that every employee is analytics enabled. These solutions will enable the staff to quickly convert complex data patterns into visual and intuitive cues that are easy to interpret and action.

Fraud and AML: According to the RBI, the Indian banking system lost Rs 8,646 crore to fraud in 2012, a 325 percent surge over 2009. Earlier this year, the Central Bank also imposed huge fines on three Public Sector Banks for violating KYC and AML norms. The challenge will only get tougher as digital channels proliferate and transaction volumes rise exponentially. Traditional detection and control systems will not be able to cope with the volume of data that each transaction generates.

Advanced analytics can help banks scan transactions in real-time to flag suspicious patterns. With the new technologies, it is even possible to monitor cross-channel behavior for deviations from the norm.

To improve the quality of surveillance, AML solutions must be integrated with KYC and watch list screening systems. Banks should also optimize their KYC and client on-boarding processes so that neither risk management nor customer experience is compromised. Over the long term, the focus must be on building an enterprise platform that offers a unified view of KYC, client on-boarding and AML and enables a more proactive approach to fraud management.

Compliance and risk: When it comes to compliance, analytics is almost a mandate. Regulatory stipulations are becoming so demanding that the conventional ‘check the box’ approach to compliance is no longer workable. In fact, in 2011 the RBI mandated an automated data flow (ADF) approach to ensure 100 percent accuracy with zero manual intervention.

This zero tolerance approach combined with the increasing complexity of reporting norms has made analytics indispensable to the compliance function. Analytics technologies will help banks combine structured and unstructured data to generate actionable intelligence that enables the proactive management and mitigation of regulatory risk. Compliance officers will be able to leverage analytics to develop a unified enterprise-wide perspective of risk and impact by connecting the dots across different layers of compliance and risk management. To extract maximum value from compliance analytics, banks will have to standardize compliance processes across business lines and geographies and adopt a source-once-use-multiple-times strategy to standardize compliance data across different sources.

Overall, investments in big data analytics technologies will help banks drive transformation across the spectrum of banking processes and functions. So far, the industry’s approach to big data analytics has been rather piecemeal, with multiple solutions being deployed across different functions and departments. The strategic long-term view should be to build a dedicated self-service enterprise-centric analytics platform that accommodates every use case and workload.

It is also important to understand that analytics is a dynamic entity that will evolve over time. As it gains traction within enterprises, the scope and sophistication of use cases will also expand. That’s where open source solutions have the advantage over proprietary platforms. As analytic aspirations grow, the cost of regularly upgrading the capabilities of a proprietary solution can turn out to be prohibitively high.