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. 

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

  • 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.

  • 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 products

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

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

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

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

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

Improve the efficiency of Underwriting by at least 40% and speed of the process by 30%, impacting the business from topline and bottom line

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

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

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

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.

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

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 products

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

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

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