With the unprecedented rise in data, the Banking, Financial Services and Insurance industry (BFSI), considered as one of the early adopters of analytics, is on the threshold of reinventing itself. Incremental risk, rising operational costs and growing competition is driving the BFSI sector to differentiate itself with constant innovation. Considering the fact that the BFSI industry has always been a data-heavy industry, Big Data analytics has the capability to provide the industry a strong differentiation factor.
With the advent of analytics most of the major banks are gradually shifting towards a culture of data-driven decision making. Almost all the data rich horizontals of any banking organization from sales to operations and even human resources can be conveniently managed by one of the many analytics techniques supported by robust tools. Data-driven business is more equipped to understand not only the customers better but also the competitors, for empowering the workforce with more useful information. Companies use a variety of tools to achieve analytics excellence. Since most of the tools available in the market today have already been used by banks, with the only change in the scope of their application governed by regulatory landscape, the main challenge is to choose the right one for the appropriate organization. The first step to identify the most apt tool from the market is to understand the main requirement and the key pain points which need to be addressed. Only a clear picture of current business problems can allow organizations to effectively choose an appropriate tool.
Each stage of analytics maturity curve i.e. from data management to prescriptive analytics requires different tools. Companies generally end up ignoring the data management system which forms the foundation of analytics. This data management can be further divided into three main steps namely Data Management System, Data Modeling and ETL. Every step requires a specific set of specialized tools from providers such as Oracle, Informatica, Teradata, for performing the concerned tasks. In order to gain tremendous value out of their data it is crucial for organizations to focus on developing dynamic reporting frameworks using tools such as Cognos, Microstrategy, Qlikview and Tableau.
Banks leveraging pure-play analytics can gain competitive edge in the market by using highly robust tools such as SAS, R and SPSS to understand and meet key business challenges. For others who like experimenting with different techniques, open source tools such as WPS and R can be conveniently used without making huge investments.
Almost all the industries are now transforming from being business intelligent to customer intelligent where they can efficiently understand their customer base. Today both structured and unstructured data can be easily analyzed by using advanced sophisticated tools and algorithms. Advanced machine learning tools such as Python and techniques such as Natural Language Processing etc. are revolutionizing and restructuring the BFSI industry.
In combination with analytical modeling techniques these tools can help the BFSI sector attain higher efficiency across all its verticals for:
- Achieving a customer centric approach to monitor compliance
- Increasing operational efficiency backed by sound data driven decisions
- Using application scorecard for mitigating risk and better decision making
- Identifying key customers and products
- Providing senior management with competitive insights to help them develop more effective strategies
Although these solutions can provide huge benefits, it is important to remember the importance of data security when selecting tools and deploying them. Companies operating in the BFSI sector should ensure appropriate measures while implementing these core analytical solutions. They should invest heavily in building proper data governance models as well as encryption tools. The focus on data security is a must for overall success.