The previous decade saw one of the biggest economic meltdowns in the world’s history and as a result financial and related services plunged the deepest in the market. After 8 years, the industry is now accelerating to its best potential with markets booming with competitive services and cutting-edge technologies.

Of all the areas, the investment into Big Data and Analytics has redefined the way modern industries think about financial services. Toos Daruvala, Senior Partner at Mckinsey once observed the future of data analytics in financial services as, “Every single major decision to drive revenue, to control costs or mitigate risks can be infused with data and analytics”.

Mitigating Risks with Intelligent Data Modelling

When banks and insurance companies talk about financial services, one of their key areas of focus is Risk Mitigation.  A study conducted by CrayonData reveals that over 90% of financial institutions believe that successful big data initiatives will “determine the winners of the future” while only 37% of them had hands-on experience with the actual implementation.

The era of information has pronounced increased digitization in the financial sector and it has opened up the way for banks, insurance agencies etc., to explore diverse, sophisticated data analytics tools and methodologies. In a quest to manage, control and reap profit with their immense data resources, key financial institutions are now focused in intelligent modelling solutions that would help them solve problems ranging from high risk fraudulences to trivial everyday operations involving data.

While addressing risk mitigation in financial services, some of the key areas that the institutions want to be addressed are

  • Risk Aggregation

After the fall of some of the powerful multinational banks and insurance companies across the world, the Basel Committee on Banking Supervision published a report titled “Principles of effective risk data aggregation and risk reporting” in 2013. This report predominantly introduced regulations that would enable banks to identify and report risks effectively to mitigate undue events and enable banks to make decisions with more agility.

Since then, many financial institutions have predominantly chosen data analytics and intelligent modelling as their choice of tool to understand, identify and report threats and risk to their services. Institutions are seeking unique tools that can be customized to understand the risk appetite with historic data, analyze the user data and create intelligent patterns that would inform decision-makers limit breaches and create information reports for the same.

  • Fraud Detection

Detecting fraudulent activities online might sound as one of the easiest risk-aversion techniques to adapt, given the abundant tools in the market, but according to a report by IDT911, over 85% of identity thefts go unnoticed.  The leaders, however, believe in building big data models out of traditional tools to create a defense system of their own. For example, PayPal might be the most popular platform for online transactions in the world, but according to CBR, they still utilize graph techniques to perform some of the highly complex fraud detection operations.

A good data analytics tool can help financial institutions to understand and identify anomalies by studying individual profiles and transactional history but it takes an intelligent modelling technique to see in between the lines and detect unusual patterns of fraud that are not uncommon in the market.

  • Regulation Management

Because of the high volatility to catastrophes, financial services are constantly bombarded with regulations that most often than not act as disruptions to the institution; a hasty implementation can even threaten the integrity of data. Therefore a well-defined data model that is built upon fundamentals and that is flexible enough to accommodate additional clauses and modifications will ensure the institutions can withstand any such contingency.

  • Advance analytics & Stock Markets

Stock markets are the real-time pulse of businesses and companies get heavily invested into grasping the trends. With advanced big data analytics and modelling tools, they can now aggregate stock patterns and analyze to understand the future trends. Furthermore, for individuals and third-party stock advisers, self-servicing sentiment analytics and Natural Language Processing (NLP) tools can help study the real-time market conditions and make informed decisions.

With an steady increase in the competition and as the lines between precaution and paranoia diffuses, institutions have to be clear about what kind of data model must their tools learn so as to deliver unique and accurate results.