How Credit Managers can Optimize the use of Big Data Analytics?October 4, 2016
Whether you call it a peril or an elixir for a financial industry, big data analytics is shaping up the way businesses are operating these days. Especially in the space of financial domain, the epoch of relying on insights to forecast or assess the risks, are nothing but bygone days.
In the world where complexity is snowballing and with data exploding exponentially every second, the ability to capture real – time information and utilise it to upgrade system response time will determine the success of risk management models.
Big Data analytics has the potential to monitor credit risks and reduce noise to signal ratios.
Trends in Big Data Analytics
- Emerging countries like India, China are outstripping Developed nations
- Gadget like mobiles, tablets, laptops are intimidating brands to integrate unstructured data and structured from these sources.
- Traditional data warehouses and systems needs to be restructured
- Compliance and regulatory data management will be top priorities
- Definitely the gaps between adopters of analytics and laggards will become more obvious in the coming days.
Optimising the use of Big Data Analytics
Real Time Information
The information customer feeds in at the time of requesting a loan, becomes obsolete over the time. Big Data analytics helps in monitoring their changing customer’s circumstances by mapping his financial profile with spending patterns, credit card repayment, payment behaviours, activity on social media platforms, interactions, etc. This helps in increasing accuracy level in predictive risk models thus decreasing fraudulent risks. The system saves both structured and unstructured data on real time, which allows manager to run ad-hoc enquiries.
Tailoring products according to varied requirements
While analytics provide deep insights about the behaviour of their customers, it also enlightens them with factors triggering their changing habits. This helps financial banks to tailor their products and align seasonal patterns with their credit offers. By displaying agility in their reactions, credit managers can seize the opportunities and safeguard themselves against various risks. Such measure will eventually help in supporting the growth of the organisation.
In the backdrop of various scams and scandals, the industry tolerance levels are declining and the high costs associated with money laundering has evoked many banks to curb anti-money laundering risk. Analytics give a holistic picture before it is too late.
Integrate voluminous date across all the Platforms
Credit and liquidity are the biggest risks which credit manager have to encounter. Systematic risks like collateral, cyber security, counterparty risk, transactional risk can be mitigated with the interconnectedness of CRM, settlement, clearing and other systems.
Get ready for the new disruption in the financial space…Be part of the next big thing…