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Restoring trust with big data analytics

Banks seeking to offer value-added services that can improve their bottom line need look no further than the mountain of customer data currently at their disposal. Financial institutions that can leverage data mining and predictive analytical tools to provide insight and detect patterns in their customers’ big data can gain tremendous business advantage.

Historically, banks have used data warehousing and analytic tools to provide end-of-day snapshots of their customers’ portfolios. Today, however, banks can use leading-edge technology to conduct proactive, real-time analysis of customer transactions to discover correlations, identify discrepancies and help their clients make more informed business decisions.

As large transaction providers, banks collect an enormous amount of data on their customers on a daily basis. Structured data gleaned from transactional products such as payments, cash management services, trade and supply chain services and payable/receivable data primarily sit untouched in banks’ data warehousing applications.

With new regulatory requirements on the horizon, banks are searching for cost-effective methods to perform regulatory checks. Banks can use big data analytics to help identify time-sensitive fraud patterns in real time that currently are performed manually or not at all. It is also critical for banks to counteract the costs of compliance and operating model challenges with revenue generated by new products and services. Big data and the technology used to forecast can play a major role for banks as they move forward.

The good news is that most banks already have next-generation analytical tools in place. All they need to do is find ways to leverage that technology to provide more visibility into big data for their customers. Banks that can mine disparate data in a holistic manner can move away from being purely transactional providers to solution advisers for their customers.

Consider, for example, a U.S.-based manufacturer that currently works with a dozen suppliers in China. As part of a value-added service, the manufacturer’s bank conducts a trend analysis of the company’s payment cycles. The analysis highlights several transaction issues with four of the manufacturer’s 12 Chinese suppliers. The bank recommends offloading business from those suppliers onto the remaining eight to reduce financing costs and mitigate risks. The manufacturer makes the changes, accelerating payments to its remaining suppliers and improving the company’s overall liquidity.

This illustrates one of many ways that banks can mine big data to help their customers lower costs and improve operating efficiencies. Banks also can take advantage of big data analytics to help their customers condense days sales outstanding cycles, accelerate loan application processes, and improve risk management.

In addition, big data analytics can help banks offer integrated solutions and improve customer loyalty. The more value-add services customers are offered, the more inclined they are to stay with their current banking partner. Banks fearful of cannibalizing existing business may actually regain customers’ trust in the wake of the financial crisis by offering proactive value-added services.

Of course, big data analytics is not without its challenges. Some banks may need to invest in additional high-performance tools to massage the big data currently residing in their warehouses. Product managers and product development specialists may require training to better understand exactly how they can leverage data and technology to truly differentiate their companies for their customers.

But banks that can leverage current technology to mine huge amounts of information in innovative ways have an opportunity to enhance their revenue streams, cross-sell products and gain a competitive advantage.


Nice summary. The challenge has been to capture the low hanging
fruit in a rapid-prototype manner, and then expanding it at a rate
the client organization can handle. Need to use existing data +
rapid data cleansing + analytics + new business processes, under the
umbrella of supportive business management, and turn that into
visible performance improvements in 9 months. Once results are
proven, it is tractable to build and improve.

Excellent post.

As a potential winner for the integration more widely within banking utilising analytics, the interdisciplinary implementation of platform design within different divisional frameworks, leading to upselling within an existing client base would potentially see target growth.

The main bump in the road with this however is the usage of data within these organisations and how that is facilitated in line with the local suite of legislation which applies to it in terms of its delivery and handling. It may not be elegant initially, but has massive scope potential.

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