In the Financial Industry, the BIG Data Analytics has moved beyond talks and experimentation to actual adoption and large budget deployments. Today Banks, Insurance agencies and asset management firms represent some of the most advanced big data users in the world and have found that big data solutions are both real and beneficial.
The three-major focus for the banks globally are Compliance, Marketing and Risk Management and in all these three areas Data Science and new generation analytics are providing key tools and techniques. Let us review the big data analytics trends in each of this area.
Regulators all over the world are demanding stricter compliance and stronger financial discipline and these compliance demands have forced banks to increase their transaction monitoring, KYC compliance (Know Your Customer), and money laundering detection and prevention efforts. The new regulations and reports increasingly create more complex requirements for detecting fraud and criminal activity. These growing requirements are now being answered through evolving Big Data analytics techniques based on Risk and Predictive modeling and classifications based on aggregation of data from different sources.
Financial institutions like Banks and credit card providers historically have possessed huge data on customer transactions, buying/selling as well as customer profiles. Using the historical as well as real time transactional data and thus leveraging larger data volumes these organizations can generate customer classification as well as buying behavior models resulting in vastly improved efficiency and faster time to market leading to better customer acquisition and retention in midst of the dynamically growing competition.
For example, it is now common in most online shopping sites to provide recommendations to buyers based on recommender systems and related analytics which integrate real-time shopping and historical customer activity with customer profiling information enhancing customer engagement.
The financial services sector has also started to utilize machine learning, which trains data to improve algorithms that make automated decisions on how to handle incoming data and queries. By leveraging their larger amounts of data, the financial institutes are learning to improve the accuracy and robustness of their algorithms and models and have started using them more confidently in back office operations such as loan underwriting, reconciliation, and Risk management i.e., underwriting and credit modeling.
Hence Machine learning which is the basis for predictive analytics, will continue to receive significant focus and it is expected that data scientists and data engineers will become important members of quantitative and risk assessment teams in Financial Industry.
However, to keep up with this evolving need, professionals with finance knowledge and Deeper analytical skills sets will be needed but due to shortage of such professionals, the industry will need to continue to aggressively compete for such talent.
It is therefore strongly recommended that Financial Executive with good business domain get into Big Data Analytics and become the professionals needed in this niche segment.