LEVERAGING BIG DATA CONVERGENCE OF MARKETING AND FINANCE : Using New Models And Big Data To Better Understand Financial Risk
LEVERAGING BIG DATA CONVERGENCE OF MARKETING AND FINANCE : Using New Models And Big Data To Better Understand Financial Risk
By Timothy Oriedo
This article looks at how marketers can help financial institutions to leverage on data when developing credit referencing .
Research indicates that individuals are intricately linked with their spatio-temporal traits , meaning that the frequency and location of a person ’ s spending has strong predictive value about their propensity to overspend or miss payments .
Lenders should look for data that can be used as reliable proxies for identity ( for example , to reduce fraud ), ability to repay ( for instance , income or current debt load ), and willingness to repay ( for example , past credit experience ).
Phone Metadata
Mobile-phone accounts provides a particularly rich potential source of data . Virtually every detail about each call , text , and request for information a customer makes is captured and stored by mobile operators .
Prepaid-minute purchase patterns can indicate a steady or uneven cash flow , and the timing and frequency of calls and text messages can indicate whether someone is working in a steady job ( for example , fewer calls between 9:00 a . m . and 5:00 p . m . may indicate that someone is working during those hours ).
The proliferation of data from mobile payments can also provide credit
‘‘ Gaining access to data can be difficult as well . In many cases , the data sets that lenders want is owned by entities ( telecommunications companies , utilities , or retailers , for instance ) that may not want - or are not allowed - to share them .’’ underwriters with rich transactional information for generating credit insights .
Social Media platforms that ride on the mobile phones penetration also provide a rich repertoire of information that can be harvested to make meaningful inferences to financial predictions .
Sources Of Data To Improve
Auxilliary sources that can be used to enrich the predictions include sources from utilities , wholesale suppliers , retailers , government , and financial institutions ’ own previously overlooked data .
Other technologies are also generating considerable raw data . Basic customer Life-Cycle Management ( CLM ) applications are becoming increasingly commonplace throughout emerging markets , enabling businesses to collect information about the frequency and character of their interactions with customers .
Point-Of-Service ( POS ) devices are used with increasing frequency
26 MAL 15 / 16 ISSUE