Risk scoring

Under evolving regulation, AI analytics helps map rapid shifts in customer behavior, ensures accurate risk assessment, and optimizes credit scoring that’s transparent, compliant and minimizes exposure.

End-to-end risk analysis

In today’s economic landscape, traditional banking service lines are under increased credit pressure, while fintech disruptors and non-banks are entering loan provision.  Our clients report new behaviors, new customer needs and new business dynamics, such as high volatility among credit consumers by sector (eg gig workers).  

SparkBeyond Discovery helps financial institutions increase decision accuracy, automate thin-file risk scoring, and enable live processing of loan applications for improved customer experience and increased market share.  

The platform automatically aligns multiple datasets, enriching internal data with external sources (footfall/mobility, geospatial (mapping), demographic, behavioural, trade and economic indicators). By automatically testing four million hypotheses per minute, the platform identifies unexpected patterns and correlations behind default and predicts credit risk hotspots within the customer base.  

Predictive models incorporate new market trends, such as shifts in the labour market caused by redundancy, furlough reduction or industry-wide downsizing.  Results are powerfully visualized (glassbox, not blackbox), with transparent loan assessment rationale fully explainable to regulators.  Data can be merged into the bank’s top-to-bottom decision-making process and augment customer teams’ evolving risk exposure expertise.

There’s vast market share potential in banking the unbanked, a traditionally high-risk ‘thin file’ sector.  As part of building a greenfield digital-first challenger bank, a Scandinavian bank wanted to serve a new customer base of ‘un-lendable applicants’ with accurate risk assessments.  SparkBeyond connected multiple previously-siloed internal and external datasets to discover 50 million patterns driving risk, generating probability models of new and traditional segments.  

Outcomes:

  • In a matter of weeks, new models increased default prediction accuracy from 3% to 97%.  
  • Client was able to bank the thin-file segment.

AI-driven analytics help investment teams make informed, real time decisions faster and improve the customer experience.

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