The Future of Risk Scoring

Technology, the future of risk scoring, alternative credit profiling, credit invisible

The Case for Alternative Risk Scoring

The days of traditional credit risk analysis are numbered and alternative risk scoring is essential to keep up with the pace of a changing market.  This change has been necessitated by the fact that companies face many obstacles in trying to determine a client’s risk profile.  A lack of data, data that is not clean and too little integration into alternative verification sources means that businesses are unable to build accurate and effective credit risk scoring models.  Add to this the pressure of competing for quality customers whilst balancing regulatory requirements and you are left with an untenable situation.

The Inadequacy of Traditional Data

Alternative data can improve access to credit, or any form of lending, for the unbanked and non-traditional segments of consumers, an enormous untapped borrower market.  Traditional scoring methods rely heavily on credit reporting agencies which makes it almost impossible for consumers without a credit record to obtain credit. These consumers are referred to as credit invisible due to a complete lack of any kind of data from which to draw a credit score.  They may very well be an excellent risk and companies are missing out on the opportunity to market to them.  Such consumers may be unable to qualify for car, and home loans although they are perfectly capable of meeting potential financial obligations.

It is undeniable that in order to address this issue lenders need to use advanced data analytics, machine learning and new sources of data.  Traditional methods of determining credit worthiness are completely inadequate to predict default and alternative risk scoring is the only way to extrapolate a comprehensive credit profile.  Moreover, leveraging social media data and integrating new technologies, like artificial intelligence (AI) can enhance the accuracy of more traditional risk assessment methods.  Combining machine learning and scorecard approaches can produce a far more powerful credit risk model.

Leveraging Non-Traditional Sources of Data

Social networks provide a wealth of information about an individual, giving savvy businesses ample data to sell and cross-sell products, basically anything is possible with so much data.  Besides your likes, dislikes and future plans, social media can be used to verify your identity and gain insight from your connections producing powerful models.  Of course, lenders cannot only rely on social media data to guide their credit policies but it is certainly the way forward in developing a more well-rounded, comprehensive profile of potential customers.

The most obvious use of this information is to generate leads and make sales.  Sales are the life blood of a business and all companies are constantly looking for a way to out compete their competitors.  Businesses can use all this non-traditional data to identify new clients and customers that are ripe for cross-sell or up-sell opportunities.

Vizibiliti Insight’s Solution

Vizibiliti Insight uses unstructured alternative data sources to build refined customer profiles segments which predict growth opportunities and risk concerns.  We are able to build new segments based on an understanding of inherent personality traits to grasp attitudes, traits and the consumers’ needs so as to respond to customer preferences.

Using proprietary algorithms Vizibiliti is able to identify early warning risk indicators and cross / up-sell opportunities. This leads to the predictions of various aspects of credit worthiness.  Alternative data inputs can include transactional, telephony, lending, verifiable data to make informed real-time decisions.

Follow Vizibiliti Insight on one of our social media platforms to find out how we are delivering solutions to one of the world’s largest telecommunication providers at the Viva Technology Conference in Paris next week.

Verizon pitch, alternative risk scoring, non-traditional data, Viva Tech Paris, credit profile, credit worthiness

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