Bigdata Credit Decisioning and Financial Analytics

BICube™ Big Data Could Replace Your Credit Score

• Scoring as a Service and Consumer Finance Technology

Current credit scoring is slow, limited, and often based on dated or incorrect information. Corrections and updates can take several weeks to post, and lenders are constantly working with old information. The scoring itself is based on formulas the industry will not disclose, leaving no way to judge its accuracy or relevance. Data is limited to static reports on past accounts that often supply dirty data, and the credit reporting agencies do not use real predictive analysis.

A good credit decision not only involves trade data, but also their financial condition, particulars about the owners, (as an example if in their 60's are they handing business over to their children, or selling the firm), security, their payment habits with your firm, etc.

The importance of 'Big Data' varies greatly on what it includes. The data may provide a good insight in the business of the firm, however the variables of the industriy must also be taken into account. For instance, Automotive industry varies greatly from Aerospace, and vice-versa, although both are in the same industry.

For all these reasons, developing a good credit scoring can be difficult, however good guideline credit scoring can be created. Especially if the creditor obtains financial analysis from firms such as Moody Analytics, (not to be confused with Moody's stock market products). These risk analysis programs can make credit scoring easier to create , monitor and amend scoring methods. Some will use credit reporting agency scores as part of overall scoring in their financial statements analysis programs, which also allow scoring points on the risk related to management, industry risk, etc.

Location data (GPS, micro-geographical), social graph (likes, friends, locations, posts), behavioral analytics (movement and duration on the webpage), people’s e-commerce shopping behavior and device data (apps installed, operating systems) are just some examples of up to 8,000 data points that are processed in real-time for any single scoring unit.

• Financial data analytics

Financial data analytics is the creation of ad hoc analysis to answer specific business questions and forecast possible future financial scenarios. The Finance sector is swamped with data. To generate the maximum ROI across your enterprise, analytics must extract value that will drive business intelligence and enhance decision making capabilities in real time delivery.

  • Embedding incremental analytics to overcome the challenges of silo’s and extensive legacy systems in the infrastructure
  • Calculating Capital – examine best practice case study applications of data insight for optimal wealth management
  • Overcoming pain points associated with reputational risk through insight fuelled communication
  • Knowing the customer: create one view of customer through collated insight
  • Achieving internal transparency through your data management platform to optimise to deliver collaborative actions in near time