Information in the BFSI sector is like the menu card of a multi-cuisine restaurant. All kinds of data on a platter, served simultaneously. To integrate the various kinds of data coming in at different velocity and at enormous volume, organizations have started to explore Big Data and related technologies as a solution. From decreasing manual dependency to increasing process efficiency, big data analytics has given BFSI a new horizon. BFSI sector has always had a huge customer base leading to immense flow of diverse information, security threats, fraudulent activity and changing demographics leading to overwhelming amounts of information accumulation. Harnessing this flow has always been a challenge for this industry. With the emergence of big data and analytics and the BFSI sector slowly but steadily embracing it, the industry is now better equipped to handle the information flow and use insights to solve business challenges.
Here are a few challenges or inherent shortcomings of the financial industry which causes some of them to lag behind, while others reap the benefit of modern technology:
Lack of focus:
According to a survey conducted by Capgemini, 90% North American banks believed that successful big data initiatives would determine the future winners. But only 37% of the banks had hands on experience with big data. Why, where and how to implement big data analytics? What value does it add to the existing structure? The BFSI sector is still seeking answers for these questions for the large part. The reason for this is mostly their faith in the existing systems and apprehension about applying new systems which may have their inherent flaws like security issues.
Working in silos:
The whole point of using big data analytics is to have seamless communication and data integration across all channels. But mostly in Banking and other financial sectors departments work in silos. Implementing one big data platform becomes challenging.
Used to traditional systems, the financial sector is slow in adapting to new forms of technology like big data analytics. This is due to mainly two reasons, security concerns and aversion to open source technology. Dealing with vulnerable data, BFSI sector relies heavily on tried and tested solutions. This sector has a tendency to adopt new technologies only when they have proven their ability to safeguard information, and don’t readily accept new tech trends.
Let’s look at a few big trends of Big Data in BFSI
From RDBMS to NoSQL:
From slow relational databases the financial sector is gradually leaning towards faster NoSQL databases. With no downtime, ability to handle semi-structured to unstructured data and a lesser complexity of process greater scalability, NoSQL is the need of the hour. As more and more industries are adopting this database BFSI is not far behind. Faster query results and ability to handle data variety of data sources, frequent updates NoSQL keeps banking and financial sector at the top of big data developments.
The BFSI sector deals with a lot of vulnerable data, making data security a challenge for this industry. Customers’ personal data, financial data, location and identity are among the various kinds of data banks and other financial institutions have to delve into and preserve from security threats. Secure database and stringent data governance provides complete control over who gets access to which data. Various components of big data are aligned with maintaining data security in storage and maintaining and upgrading with various compliances including PCI and PII, Dodd Franks etc. Another important part of this is risk management. Data lakes can serve as converged regulatory and risk (RDARR) hubs. Thanks to predictive data analytics, it is easier to sort through customer history and other information to filter out risks and fraudulent activity before investing.
Gartner predicts that by 2018 customer digital assistants will recognize individuals by face and voice across channels and partners.
Moving things from on premise to a more secure and flexible platform on cloud is also a big trend right now for those using big data technology. The data security on cloud is more stringent with security elements already present on cloud, also the storage is flexible enough to accommodate growing amounts of data. The immense storage capability of cloud never falls short. Financial institutions like World Bank and Tesco Bank have recently invested in cloud services.
This trend has taken the automobile insurance industry by storm. Calculating risk was never this easy. Previously whether a customer was a good or bad driver, according to their general demographics they had to pay the same premium. But now, thanks to telematics, a small device attached to customers’ car to gauge what kind of driver he/she is, risk can be calculated fairly. Rendering customers to pay based on how well or badly they drive.
The more services the BFSI sector offers, the more avenues of investment opens, and it simultaneously increases the risk of fraudulent activity. Various new payment channels, online payment options including digital wallets have opened new avenues for customer comfort as well as risks of fraud. With new payment methods, it increases verification of customers, with increase of verification details that increases data volume, requiring big data analytics. Money laundering, fake identity and other fraudulent activities lead to direct and indirect financial losses for any financial service provider. From reputational impact to losses of money to address the problem, frauds have a major impact on business. Thanks to big data and real time stream processing analytics BFSI sector is now able to better detect fraud and save themselves and their customers from loss. Now if a customer’s credit or debit card is being misused, with real time data of geographical location and time the bank can alert the customer instantly, giving the customer a chance to take prompt actions. Comparing geographical locations of customers and card usage, spending patterns and other vital information financial service providers are able to better detect and take action against frauds. According to EY’s Global Forensic Data Analytics Survey 2014 showed that 72% of respondents believed big data technologies had a role to play in fraud prevention and detection.
The health insurance provider Discovery health uses big data to detect fraudulent activity. They can figure out if a health service provider has charged more money from the client than the actual standard amount.
The BFSI sector is learning to depend on machine learning to detect frauds and other anomalies in transactions. Decision makers now have insights at their fingertips with smart data discovery and simple big data tools. Keeping up with new regulatory compliances have become easier for financial institutions with regular alerts and updates. Lastly, with the growing number of international and local financial institutions embracing the big data wave, the difference will be marked between the pioneers and the laggards more prominently.
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