How AI Empowers Credit Access For Thin Credit Individuals

Introdução 

Lease-to-own financing options open up access and purchasing power for those with bad credit or no credit. In the US, Shield Leasing offers simple, straight-forward options to help automobile owners get the tires, wheels, and minor auto repairs needed to keep their vehicles on the road.  Shield Leasing’s brand promise is an easy application process with instant approval for applicants with low to no credit.

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Desafios enfrentados

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1. The applicant pool was vast and diverse with low to no credit, so the data used to evaluate credit risk had to be orchestrated from many different legacy data systems - traditional and non-traditional - in different formats - APIs, databases and spreadsheets. Integration with legacy is challenging and time-consuming.
2. Easy application process with instant approvals meant Shield Leasing had to do more with less data and make decisions in real-time. ​​The accuracy and completeness of the data used in the models are crucial for their reliability.
3. Shield Leasing was scaling their business so the applicant profile would change over time, so migration from a static rule based decision process to a dynamic AI based decision engine was an important goal.
4. Shield Leasing wanted to scale and modernize their 360 degree view of customers and business using modern BI applications. 

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Solução implementada

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‍Data orchestration 

Os dados internos e externos de várias fontes - de terceiros, internos e fornecidos pelos utilizadores - foram orquestrados e combinados e analisados para compreender melhor os candidatos, estudando as distribuições, os padrões e as anomalias nos dados. 

Criação de caraterísticas específicas para a avaliação de riscos 

Quanto melhores forem os dados fornecidos aos modelos para a tomada de decisões, melhores serão as decisões. Para reunir as informações mais relevantes para treinar o modelo, foram criadas caraterísticas adicionais, como rácios, velocidades e contadores de frequência, a partir dos dados de entrada disponíveis. Por exemplo, caraterísticas padrão como o rácio dívida/rendimento ou caraterísticas não tradicionais como a confiança no correio eletrónico. Isto foi feito sem problemas, utilizando as capacidades de AutoAI da plataforma RapidCanvas. 

Modelação automatizada e IA explicável 

The AutoAI platform automated the creation of the best possible model to predict, at the time of credit application, which applications are risky. With this white box approach, the internal working of the model and the importance of each factor used for prediction can be easily explained. In situations involving credit risk, it's important to understand not only if someone is risky but also why they are risky. Explainability is important for ensuring accountability, fairness, and transparency in automated decision-making systems.

What-if Analysis: Credit evaluation depends on individual applicant profiles as well as the macro economic environment. It is important to be able to simulate ‘What if?’ situations. Play with different features and find how they impact predictions.

Aplicação abrangente de business intelligence  

Interactive data apps were generated for business users to review credit predictions and make data-driven decisions. With increased visibility into the risk profile of each applicant, the Shield Leasing team was able to better understand the factors that influenced credit and trends arising from the data. 

Actualizações contínuas do modelo 

With an ever-increasing pool of applicants and changing trends, the model is continually updated to ensure effective predictions are always available for the team at Shield Leasing.

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Results and Benefits:

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Capacidade de escalar, reforçando simultaneamente a promessa da marca

Shield Leasing’s brand promise is an easy application process with instant approval for applicants with low to no credit. AI and machine learning allowed Shield Leasing to scale its customer base while ensuring the brand promise could be reinforced.

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Aumento das receitas 

Shield was able to detect risky credit applications and positively impact their revenue, to the tune of 10%. 

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Improved  credit risk management  

With the insights provided using dynamic real-time machine learning models to predict future outcomes, Shield Leasing could better assess and manage risk both during the credit application and the ongoing payback period.

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Conhecimentos mais profundos sobre os clientes 

The interactive data apps gave the Shield Leasing team a deeper understanding of customer insights. The data apps showcase a 360-degree view of each customer, segment and cluster of users to better understand groups of customers with similar patterns and behaviors, and to analyze and explore alternative outcomes.

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