The digital lending life cycle space in India is growing exponentially. The Research and Market Report revealed the Indian Digital Lending Platform Market is anticipated to grow at a CAGR of 27.95% and grow to $2507.55 million by 2027.
Artificial Intelligence (AI) and data analytics are helping lenders sustain and fuel this growth via a streamlined lending life cycle. While AI enables the automation of repetitive manual tasks, saving time and labour expenses, data analytics offers valuable insights and helps predict borrower behaviour and formulate strategies.
Financial institutions globally are embracing AI-based systems for the various benefits they offer, with over $217 billion spent on AI applications in 2021 alone to help them prevent fraud and assess risk.
In this blog, we explore how integrating AI and data analytics can help lenders transform the lending life cycle and offer numerous benefits.
Lending Life Cycle: An Overview
The lending life cycle involves multiple stages and stakeholders- lenders, borrowers, underwriters, credit bureaus, third-party service providers and regulators. Let’s elaborate on its key phases:
This phase sets in motion the loan origination process; the applicant inquires about the loan from the prospective lender and submits the required documents.
2. Loan Application
If the applicant qualifies for the pre-qualification stage, they submit the loan application, along with the documents, as informed by the lender.
3. Document Processing
The loan application is sent for processing to the credit department. The lender asks the applicant for additional details( if required) and verifies the documents.
4. Underwriting and Evaluation
This is a crucial phase in the lending lifecycle; the credit appraisal department assesses the applicant’s eligibility, creditworthiness and risk profile.
5. Loan Approval and Disbursement
Once the lenders are satisfied with the applicant’s credit health and the paperwork, they approve the application and disburse the loan.
6. Loan Repayment and Closure
This phase deals with tracking and monitoring the loan repayment process and taking suitable steps in case of delays in repayment or missed instalments. Monitoring loans closely after disbursement is integral to a lender’s success. After complete repayment, the loan is closed in the lender’s record.
How Artificial Intelligence and Data Analytics Facilitate Lending Life Cycle Transformation?
Technological innovations are transforming the entire financial landscape. Below are five ways AI and data analytics can positively impact the lending lifecycle.
1. Streamlines Loan Processing
AI can help financial institutions automate various steps in the lending phase, like data compilation, verification of documents, credit assessment and more.
Lenders can reduce costs and improve efficiency with workflow automation in the entire lending life cycle. Automation helps reduce processing time, minimises errors, and streamlines loan processing operations; optimising loan origination procedures with data analytics improves business performance.
Document identification and data extraction solutions help lenders accelerate the KYC process by quickly extracting information such as faces, names, numbers, addresses, transactions, and more in real time.
2. Enhanced Credit Evaluation
AI-powered machine learning (ML) models help lenders improve risk management by extracting relevant data, identifying trends from complex unstructured documents and correlating these data points meaningfully.
During the lending life cycle, large volumes of data processed with data analytics tools can help you accurately understand the applicant’s creditworthiness. It also helps lenders consider alternate parameters like online buyer behaviour, utility bill payment history, demographic aspects and more to get a more holistic borrower profile.
AI and data analytics can predict loan defaults by analysing borrower data, economic indicators and market trends, which helps lenders mitigate risks and optimise portfolio performance by
For example, Finezza’s Bank Statement Analyser is an AI-based tool that helps analyse borrowers’ bank statements. It determines financial patterns, assesses the borrower’s financial stability and documents authenticity.
3. Better Loan Monitoring
Lenders can manage risky accounts and potential defaults with the help of insights which AI-powered algorithms provide by analysing data from multiple sources. In the lending life cycle, real-time monitoring of loans helps lenders stay updated on any possible defaults and take appropriate action quickly.
Also, data analytics tools allow Lenders to gain a deeper and more nuanced understanding of their customers. Effective intervention for debt collection based on data sources like debtor demographics, account activity, and risk ratings can help reduce defaults.
For instance, the Collection Delinquency Management system helps automate and simplify loan collection for lenders with a sophisticated, customer-centric solution that leverages AI and data analytics.
4. Promotes Financial Inclusion
Some applicants may find it challenging to access credit due to biases inherent in the system due to manual credit appraisal and loan processing; their loan application may not cross the first stage of the lending life cycle.
AI and data-driven algorithms base their decisions on objective data and other intelligent inputs, not perceptions or human judgement. This allows deserving applicants to avail of credit and promote financial inclusion.
Traditionally, banks use credit scores to evaluate a potential borrower, which may leave out a large chunk of the population from the credit availability. Using alternate data to assess an applicant’s creditworthiness can help applicants with no credit history avail of credit.
Financial institutions can reduce the chance of bad loans, customise solutions and increase their customer base by employing AI and data analytics.
5. Improved Customer Service and Debt Recovery
In the lending life cycle, smart chatbots offer clients comprehensive and personalised self-help solutions with the help of AI. They can update borrowers on their loan status, closure and personalised solutions. Virtual assistants and chatbots can also guide borrowers through the loan application process and improve their chances of loan approvals.
Loan chatbots make debt recovery simpler by sending timely payment reminder messages. Real-time updates about EMIs improve the chance of borrowers repaying promptly. Chatbots also reduce the effort and time required by field agents for debt recovery.
As per a Times of India report, AI-powered voice bots have achieved 40% higher engagement rates, in some cases significantly reducing human calling costs and overall collections time.
Lenders can improve their operations, make better credit decisions and provide better customer service throughout the lending life cycle with the help of solutions that leverage data analytics and artificial intelligence. Financial institutions can offer customised solutions, facilitate financial inclusion and increase their customer base by employing AI and data analytics.
Software solutions incorporating artificial intelligence and data analytics, like the suite of products offered by Finezza, can help lenders in each stage of the lending life cycle, benefitting lenders and customers alike.
Contact us to know more.