Big Data for Financial services
Financial industry has unique operational challenges like compliance and regulatory obligations which must be carried out in efficient and cost effective way. Compliance and risk management strategies take paramount importance. To achieve this one must ensure data governance and possess the knowledge of supply chains, and also make sure that data silos do not exist.
Sentienz data platform offers data governance, data security and holistic view of data which accelerates the process of minimizing the risk and effectively conform to the regulatory compliances.
Bigdata Experts at Sentienz understand the game of scale, financial services companies have a need for data governance, security and massive historical data. Also we respect the existing IT Infrastructure and allow smooth adoption. Our platform enables you to fasten the road to advanced analytics.
Bigdata the “Opportunity”
Complex data processing systems support bank and trading activities and each transaction generates more data for analysis. Financial services have a ocean of information but they are all in different data repositories, there is a need to see this data together for coming up with real time risk mitigation strategies. Market trends, actuarial models, credit data, loan data are certain data points in financial services which helps in understanding trends thus accelerate decision making
Bigdata Financial services Usecase
Identify loan defaulters and screen fresh accounts
It’s a time taking task for Bankers to screen a potential customer, also they have stringent rules. Now with both internal and data from external risk scoring services in real time we can suggest his eligibility for opening a new account.
Also analyzing previous scores and current payment trends one can determine the loan repayment capacities of a customer.
Minimize Risk & Financial data governance
Increased regulation is placing more pressure on financial institutions to improve data governance.
To minimize risk and promote compliance there is a need for all performance data in one place for easy access.
Half-life solves the purpose by brining data from different sources into one common governed and secure storage for e.g.
Integrate historical and real-time financial data
Banks have ocean of data in terms of operational, transactional and data that is residing with them historically. There is a need to combine real-time operational data and historical data because historical data adds necessary context to the current customer engagements which will help in fraud detection.
Customer Insight and Service
In Financial services even a 0.01% variation in interest rate offer to the customer makes a huge difference hence this space is highly competitive. So any kind of information related to customer is an advantage.
Understanding the spending pattern for e.g spending on home loans can trigger a personalized message offering a top up loan.
Sentienz data platform allows custom algorithms apart form standard ML algos to be run on the customer data which will provide insight to individual financial needs.
Fraud Detection and Security
Detecting anomalies in transactions is very important for e.g. a sudden huge transaction or a transaction from an geo location uncommon to the user. Banks are prime targets for fraudsters especially cyber criminals.
Here again, historical data coupled with real-time data will help in detecting erroneous patterns. Thus these institutions can tighten their security against future attacks.
External data related to competitive rates coupled with risk data captured internally helps developing smarter pricing and lending strategies.