Cignifi Uses Mobile Use Data to Determine Credit Scores
Simal Yilmaz — April 26, 2013 — Tech
References: cignifi & springwise
Credit score determination for people who don't have a credit history is understandably hard and risky for institutions that require it.
Founders of Cignifi have figured out a way to determine credit ratings of these people through behavioral analysis. The data is conceived from people's mobile usage data.
Cignifi stresses the privacy of people and states that the credit score data are created “from a combination of behavioral and time-related attributes, but do not rely on any location data or content information from voice calls or texts.” The service is certainly one that will be put in used by banks in no time.
Although indirectly, an innovation such as this reminds us all the importance of mindfulness we present for all our decisions as technology will predictably to find a way to track them for various reasons in the future.
Founders of Cignifi have figured out a way to determine credit ratings of these people through behavioral analysis. The data is conceived from people's mobile usage data.
Cignifi stresses the privacy of people and states that the credit score data are created “from a combination of behavioral and time-related attributes, but do not rely on any location data or content information from voice calls or texts.” The service is certainly one that will be put in used by banks in no time.
Although indirectly, an innovation such as this reminds us all the importance of mindfulness we present for all our decisions as technology will predictably to find a way to track them for various reasons in the future.
Trend Themes
1. Behavioral Credit Score Determination - Using mobile usage data to determine credit scores for individuals without a credit history opens up opportunities for innovative approaches to assessing creditworthiness.
2. Privacy-focused Data Analysis - Exploring credit rating creation methods that prioritize privacy and utilize behavioral and time-related attributes instead of location data or content information can disrupt the traditional credit scoring industry.
3. Tech-enabled Financial Inclusion - Leveraging mobile data analysis to determine credit scores can provide better access to financial services for individuals who lack a credit history, creating opportunities for disruptive innovation in the financial inclusion space.
Industry Implications
1. Banking - Banks can adopt innovative credit scoring methods based on behavioral analysis of mobile usage data to expand their customer base and offer financial services to individuals without a credit history.
2. Data Analytics - The growing need for privacy-focused data analysis techniques and algorithms to derive credit scores from mobile usage data presents disruptive innovation opportunities in the data analytics industry.
3. Financial Services - By utilizing innovative credit scoring methods that rely on behavioral analysis of mobile usage data, financial service providers can cater to underserved populations and drive financial inclusion.
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