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
this one must ensure data governance and possess the knowledge of supply chains, and also make sure that data
do not exist.
Sentienz data platform offers data governance, data security and holistic view of data which accelerates
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.
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
Financial services have a ocean of information but they are all in different data repositories, there is a need
see this data together for coming up with real time risk mitigation strategies. Market trends, actuarial models,
data, loan data are certain data points in financial services which helps in understanding trends thus
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
Also analyzing previous scores and current payment trends one can determine the loan repayment
capacities of a
Minimize Risk & Financial data governance
Increased regulation is placing more pressure on financial institutions to improve data governance. To
and promote compliance there is a need for all performance data in one place for easy access. Argo solves the
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
There is a need to combine real-time operational data and historical data because historical data adds
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
top up loan.
Sentienz data platform allows custom algorithms apart form standard ML algos to be run on the customer
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
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
can tighten their security against future attacks.
External data related to competitive rates coupled with risk data captured internally helps developing
and lending strategies.