The Four V’s of Big Data for Credit Scoring
It has been established with quite a mark on the stone that data is essential for Financial Institutions to take important decisions pertaining to their business. Strategic solutions that are backed by powerful data definitely go a long run and instill confidence amongst the shareholders. Having established its prominence, there are four characteristics of Big Data that should not be missed, calling here as The Four V’s –
1) Volume –
Any analysis that has to be done must involve using ample data samples to avoid taking incorrect actions based on outlier performance. Therefore, it becomes crucial for a Financial Institution to handle the storing of the massive volume of data; otherwise, the issue can pose as a major bottleneck for the firm.
2) Velocity –
The velocity aspect refers to the rate at which data is being collected, and the speed at which that data can be collected, stored, and analyzed.
In an example, a bank can use the velocity of big data sources to help detect customer leads. If the bank detects that a customer has been searching on their website, it can determine how deep the level of engagement was and how long the visitor stayed on their website. Using a cloud-based solution, the bank can ascertain how many pages deep into the website the visitor viewed if any tools were used, and where the drop-off point occurred. The bank can then use this information to prioritize customer contact.
Another aspect of data velocity is the ability to build predictive models at a much more rapid speed and use much more data than before big data sources were available.
3) Variety –
With data getting generated on almost every platform and the onset of the heavy individual digital footprint — Unstructured Data becomes an untapped source that can provide very rich information about a customer.
Banking data can include information from sources such as mobile banking applications, telecommunications sources, social media data such as credit applicant’s connections on LinkedIn, clickstream data, application programming interfaces (APIs) via the cloud, and voice response logs.
In the middle of it, the only task that a Financial Institution has to accomplish is to accommodate Unstructured Data as efficiently as traditional Structured Data.
4) Value –
For businesses that loan money, value means making money. Banks and other lending institutions can derive value from big data and use big data sources to remain competitive. Some of the initiatives that can help are –
a) Eliminate Data Silos
b) Realize that it takes time to properly analyze big data sources
c) Recruit highly trained data scientists
d) Invest in software and hardware that is needed to properly collect, store and analyze big data sources
Source — Intelligent Credit Scoring by Naeem Siddiqi
Please feel free to reach out to me at https://www.linkedin.com/in/anshika-parihar-bb69a7145/ or p17anshikap@iimidr.ac.in in case of anything that I might have missed.
Thanks for reading, Cheers!
