#WednesdayInsight: Revolutionizing Credit Underwriting with AI and Data-Driven Approaches

#WednesdayInsight: Revolutionizing Credit Underwriting with AI and Data-Driven Approaches

Introduction: 

The lending landscape in India is diverse and multifaceted, encompassing a variety of platforms and institutions. These include traditional banks, platforms catering to Small and Medium Enterprises (SMEs), consumer-focused lending platforms, retail card providers, platforms with a focus on financial inclusion, and peer-to-peer lending platforms.

Within this landscape, the role of data and Artificial Intelligence (AI) is pivotal in shaping decision-making tools for lenders using comprehensive digital lending software to streamline their lending operations. 

The ecosystem is continuously evolving, with the emergence of new entities further enriching the diversity of the lending environment. Data providers and underwriting platforms play a significant role, emphasizing the indispensable value of data in the ecosystem.

Challenges in Data Utilization and The Role of Alternate Data

The availability of data is abundant, yet its effective utilization remains a significant challenge, especially for banks. Emerging platforms are leveraging data for early warning systems, enabling the generation of real-time cases and enhancing responsiveness in lending processes.

Challenge 1: Underutilization of Available Data

Many entities, particularly banks, find it difficult to harness the vast amounts of available data effectively. This underutilization hampers the optimization of lending processes and decision-making. Read more

Challenge 2: Misalignment of Data Models with Real-life Applications

Data models, developed by data scientists, often do not align well with real-world applications. These models can sometimes be too restrictive, limiting their applicability in lending scenarios. Read more

Challenge 3: Bridging the Gap between Human and System Thinking

There is a pressing need to reconcile human thinking with system thinking in data utilization. This reconciliation is crucial to ensure that the insights derived are coherent with human understanding and are applicable in real-world scenarios. Read more

Navigating Through Financial Inclusivity By Using Alternate Data

  • Advancing Financial Inclusion through Innovative Data Structures
  • The Global Imperative of Financial Inclusion
  • Innovative Approaches to Credit Access
  • Accelerating Financial Inclusion through Tailored Offerings
  • The Pivotal Role of Alternate Data

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Understanding Structured and Unstructured Alternate Data

Definition:

  • Alternate data refers to any digitally available data that is not considered traditional data.
  • It includes mobile internet usage, electricity and utility usage, social and digital footprinting, and online shopping behaviours.

Categories:

  • Structured Data: Requires minimal effort to curate and process.
  • Unstructured Data: Includes social media footprints, internet usage, email, and text messages.

Importance of Proper Utilization:

  • It’s crucial not only to collect but also to curate this information properly.
  • Applying the right analytics, AI, and ML is essential to draw accurate inferences.
  • Addressing hidden patterns and systematic biases within this data is vital.

Utilization of Structured and Unstructured Data in Underwriting and Portfolio Management

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Levels of Data for Lending Institutions

Lending institutions perceive all data as credit data, encompassing both financial and alternate data. There are distinct levels of data that these institutions utilize to gain insights and make informed decisions.

  •  Financial Account Data
  •  Payment Data
  •  Levels of Reliable Alternative Data Sources (Level 2 Data)
  •  Non-Financial Data and Customer Behaviour (Level 3 Data) 

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Building a Comprehensive Alternate Data Ecosystem for Financial Inclusion

  •  Establishing a Robust Ecosystem
  •  Addressing Financial Inclusion
  •  The Role of Advanced Analytics and Machine Learning
  •  Adapting to Global Uncertainties

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The Crucial Role of a Robust Data Strategy in Customer Value Maximization

Developing a comprehensive data strategy is pivotal for organizations aiming to enhance the lifetime value of their customers. A well-rounded strategy revolves around gaining profound insights into customer behaviours, preferences, and needs. It is essential to incorporate provisions in the data strategy for new data that will emerge in the future and to analyze historical data to draw meaningful inferences and make informed decisions.

  • Consistency and Precision in Data Strategy
  • Alignment with Business Goals

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The Power of AI and ML in Lending Decisions

AI (Artificial Intelligence) and ML (Machine Learning) have transformative power in lending, by programmatically combining diverse variables, and creating predictive features. Automated tools are vital for underwriting and scoring. These technologies work alongside business rules, ensuring effective customer service and alignment with business goals.

Role of Business Rules and Policies in Lending Decisions using AI & ML

  • Significance of Business Rules in Lending Decisions
  • Understanding and Labeling Data Correctly

Incremental Improvement and Monitoring

  • Enhancement through Incremental Improvement
  • Real-Time Monitoring for Desired Outcomes

AI and ML in Lending Decisions and Model Transparency

  • Demystifying AI in Lending Decisions

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Real-Time Decision Transparency

The illustration of real-time transparency in lending decisions reveals the impact of various variables, both positive and negative, on the decision-making process. This insight is provided for every lending decision in real-time, enabling hyper-personalized customer engagement and precise customer segmentation. Such transparency is instrumental in driving marketing strategies, cross-selling, upselling, and collection-type activities, allowing for more targeted and effective customer interactions and interventions.

Model Governance & Regulatory Compliance

Model governance is crucial, necessitating institutions to have a profound understanding of how their models operate and to maintain comprehensive documentation to ensure compliance with regulatory bodies. The documentation should elucidate the functioning of the models, and it is imperative to measure this against actual outcomes to verify that the model is operating as anticipated. This approach ensures the reliability and compliance of the models, fostering trust and adherence to regulatory standards.

Portfolio Performance Monitoring

The integration of a portfolio performance monitoring system with the scoring and lending decision system is crucial, as it generates insightful portfolio analytics. This system provides analytics on various aspects such as 

  • portfolio growth by customers and revenue, 
  • portfolio disbursements, 
  • bad rates at different DPD levels, and 
  • financial margin. 

Such comprehensive analytics allow for an in-depth understanding of the portfolio’s performance, enabling more informed and strategic decision-making to optimize portfolio outcomes.

Fair Lending and Model Improvement

Fair lending is crucial, and models must align with KPIs. Real-time graphs, fed by underwriting, monitor demographics like caste and religion, triggering alerts for deviations. Continual improvement, analyzing outcomes, and adapting to customer behavior and conditions enhance model accuracy, ensuring it reflects evolving behaviors and circumstances.

Conclusion

AI and ML are transformative tools in lending, enabling better data analysis and customer profiling. Embracing AI is an evolutionary process, not requiring a complete system overhaul. By understanding, structuring, and integrating data effectively, we pave the way for gradual and sustainable advancements, leading to more informed and equitable financial solutions.

Gunjan Varde

Data Science Lead | Credit Risk Consultant | I help Business To Drive A 2x Increase In Profit

1y

Thanks for sharing Mani Parthasarathy

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