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Can Bankers Sleep Better Knowing Customer Data Is Safe Through Analytics Backed Digital Innovation?

Anil Somani, Chief Transformation Officer, Altimetric

In his career spanning over three decades, Anil has held key positions across an array of companies such as National Semiconductor, Cisco, eBay, IBM and Visa, prior to joining Altimetrik in 2017. He boasts of an exceptional track record of executing technology and business growth strategies mainly across the BFSI vertical.

Data security is one of the top reasons for bankers to lose sleep in this age where data and analytics can unlock a gold mine of insights that can magnify the potential of business growth. The beauty of this paradox is that same data analytics can be used for top line growth by creating business differentiation as well as bottom line protection by minimizing exposure and disruption.

Banking and financial services companies have access to humongous data sizes and are always exposed to risks that can lead to heavy financial and reputation loss. Trust is the biggest factor in this business. Apart from monetary losses, regulatory scrutiny, brand impact and customer trust impact is a big deal.

However, managed in proper ways, this data can help banks understand customer personas, their banking behaviors, identify revenue leakages, improve operational efficiency and so much more. It is therefore of utmost importance that data engineering is carefully managed end to end, from source to insight.

1. Let’s look at a few checkpoints that the CIO of a bank should consider:
2. Metadata tagging and provenance that will manage authenticity and track of data
3. Analysis of access patterns of internal data, both isolated and aggregate
4. Analysis of usage patterns of data e.g. during eCommerce checkout, customer payment instrument selection and spend.
5. Analysis of internal and external identity and behaviour
6. Trend Analysis of seasonality data and cause – effect patterns
7. Analysis of IPs, Geocodes, device fingerprinting
8. Analysis of obfuscation, masking and encryption effectiveness of data at rest and in transit.

What would an advanced model do?
Beyond this, the data engine must look at real-time data analytics, create sophisticated models using Machine Learning, establish deep learning neural networks for detection and self-healing. The engine should also be capable of thwarting Distributed Denial of Service (DDOS), insider threats, creating baselines and real time analysis against that.

Do more with data
With a secure architecture in place, we can improve the system to drive higher business outcomes and impact the bottom-line. Increase the data analysis aperture to include social data and market data in addition to internal data that gives the business a larger scale of customer understanding and segments. We can inject human analysis and intelligence by vetted crowd sourcing, external data sources and collective combat along with competitors.

The problem that many businesses are facing today is that they get into data in a big bang way, end up without relevant outcomes and put the data at risk. It is a mistake to take-up everything at a go. Data has to be handled with a roadmap in a step-by-step process for maximum security, governance and ROI.

The solution to reaping benefits through data analytics while ensuring maximum data security is not a one-size-fits-all or one-time implementation. It’s a continuous evolution process that has to be governed and monitored regularly. The system architecture therefore, has to be agile and resilient.

There is immense value that BFSI players can drive out of data and analytics – from customer segmentation, improving sales & marketing efficiency, to driving faster and accurate decisions. Customers are expecting banks do scale-up faster and keep their data safe. To avoid the nightmares, it is super important for the CXOs in the BFSI domain to rethink their data strategy!

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