For lenders, growing a profitable book involves  a great customer experience, relevant new products , and simultaneously a mature and robustly adaptable credit decisioning and risk management strategy

The Lending business is based on monitoring large amounts of data, with the added complexity that the relevance of the data is a subjective decision; a datapoint could be irrelevant or the portend of portfolio wide trouble. The dynamicity of the environment is further exacerbated by the fact that established frameworks of  assessment are being upended by  diverse new datasets becoming available. The regulatory frameworks, risks and compliance frameworks, and customer safety remain abiding considerations.

Autonomous systems are able to absorb these vast amounts of data, across multiple categories and are able to rigorously and relentlessly scan for present and emerging risks, along with agile decisioning and flexible risk assessment systems that allow for growth.

Virtual attendance

Improve credit approval times to build customer trust and relationships while keeping delinquencies low

Provide swift credit approvals through automated risk assessment and decisioning

Achieve consistent, swift and high quality credit decisioning and approvals through automated decisions delivered with high precision and recall

Efficiently process approvals at a reduced cost, with a higher accuracy and reduced turnaround times

Maintain credit decision quality even at peak seasonal demands by minimizing human error under excessive workloads

Deliver near real time decisions to support the customers' needs 24X7 , for certain categories of credit needs

Intelligently assess documentation required to minimise iterations and ensure a great customer experience

Virtual attendance

Reduce delinquencies, enact timely mitigation strategies and minimize defaults through comprehensive and autonomously updating decisioning system

Improve risk assessment and take better credit decisions

Using many dimensions beyond what rule engines can ingest, identify risk factors and discover emerging flags, through learning from historical day  as well as aggregate portfolio profiles.

Continuous and comprehensive coverage of individual transactions

Seamlessly factor in newer flags or clusters emerging as significant risk criteria beyond rigid rules requiring updating

Grow business by going beyond traditional demographic or income level data to make credit decisions

Use learnings from individual customers that might portend portfolio level impact to make more considered decisions and overall policy changes

Virtual attendance

Adapt to dynamic situations caused by new product launches or changing risk environments with no disruption to core decisioning systems

Stay agile with a robust and always updated credit decisioning engine

Improve product launch, design and focus decisions enabled by lending engine AI whose learned capability, from absorbing vast historical datasets across products and customer sets, spans across product

Swifter new product launch with minimal configuration to credit approval system

Dynamic updating of credit policies through ongoing learning if changing risk and customer environmentsy

Deeply assess customer profile and then match to product on offer to make personalized pricing decisions

Discern need gaps in the product and appropriately offer design products

Virtual attendance

Effectively communicate with customers to appropriate content and channels, discern profile changes to increase engagement levels.

Engage with customers to ensure effective loan repayments and identify troubled early

Combine enterprise current and historical data across products to monitor repayments, identify required interventions, and use appropriate messaging and channels to engage

Monitor incoming instalment data to identify areas or specific customers requiring attention

Use appropriate communication channels, volume and messaging to maintain asset health;

Assess any change in customer profile and restructure loan to suit new situationms

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