The power of computers to streamline operations was recognized by banking and financial services early on. They used it to manage everyday tasks that were tedious and time-consuming. However, there remain processes that require manual attention and effort. The loan origination processes have many activities that use manual labour.
What is loan management?
Loan management is the cumulative of all processes in the loan lifecycle- origination, servicing and administration. Application processing, underwriting, customer service are among the essential activities in loan management.
The loan management workflow traditionally relies on heavy paperwork, from the beginning, with the collection of loan applications and documents, to manual data entry for computation of scores to the end, approval and disbursal of loans. Although human supervision is essential for these processes, their dependence on manual activities has caused inevitable delays, inaccuracies and risks.
AI technologies have equipped business leaders with tools to transform processes, boost efficiency and delight customers to retain a competitive advantage. The challenges of legacy loan management systems glaringly state the need for AI technologies to transform their systems.
Challenges
- Data Extraction
There is a list of documents from customers that are affixed with the loan application to be considered. These documents are meant to support personal credentials, financial stature, repayment history etc. Lending entities extract data from financial statements, and identity documents to address two critical needs: assess the borrower’s creditworthiness and compliance with KYC regulations. The documents come in a variety of layouts with non-standard terminology making the manual data extraction process all the more difficult, error-prone and time-consuming.
- Data Analytics
The applicant’s creditworthiness evaluation is simplified by convention, to a few basic parameters. With time and technology, defrauding attempts have become sophisticated to overcome these evaluations. Risk assessment needs a multi-pronged approach to mitigate these risks. Human limitations often hinder an in-depth, comprehensive analysis to detect such risks. However, ensuring transparency and objectivity in risk assessment requires tools that are equipped to detect risks.
- Administrative Controls
A centralized, holistic loan management system is often absent in organizations using traditional underwriting processes. The systems are incoherent, lacking a streamlined approach towards business goals. The loan origination depends on multiple teams that are interdependent on each other, relying on managers to coordinate a systematic flow of information among all stakeholders to drive the process.
Let’s now look at the positive transformation AI brings to each of these aspects.
AI-Powered Data Extraction Techniques
Unearthing Insights from Documents
Customers’ documents are often hundreds of pages long, in various formats and differ in image quality. AI-powered applications employ ML algorithms to inspect records, authenticate the information, extract data from financial statements, and accurately categorize data points. These tools can identify key financial values from bank statements, passbooks, payslips, balance sheets, profit and loss and cashflow statements, to run essential computations and ratio analysis into a compiled summary/dashboard. They also use powerful computer Vision- aided text recognition tools, to process images and photographs of low quality.
AI Analytics Solutions
Empowering Data-backed Decision-Making
AI-powered Loan Management System
Ensuring Friction-free Workflows
AI applications are designed to automate key tasks that often require manual intervention such as credit evaluation and risk profiling. They can be used to streamline the process to accelerate loan approvals with minimal human intervention. AI improves customer experience by enhancing loan servicing and collections management by optimizing repayment plans suitable for the customer, identifying and predicting potential defaults and suggesting strategies to minimize risks.
Benefits
Business leaders in the financial services and banking sector can benefit from AI technologies in several ways:
Efficiency Gains
The primary advantage of faster, smoother AI-driven workflows is the operational efficiency with poor-quality data. With AI-powered data extraction and analytics, many applications can run simultaneously without the need for a larger workforce. This leads to a significant reduction in cost and resource optimization.
Elimination of Credit Risks
Data- driven, thorough loan approvals are essential for profitable operations. With AI analytics solutions, accuracy in processing is ensured through systematic examination and verification of data. This enables early detection of suspicious, fraudulent actions or patterns, in the loan cycle, facilitating safe lending decisions.
Scalable Operations
AI applications are easy to upgrade without compromising functionality or performance. Even as business demands grow, the solutions are effective at tackling new challenges.
Enhanced Customer Satisfaction
AI driven analytics give a nuanced view of customer’s needs. They enable personalised customer experiences by tailoring products and services to specific needs. Lenders can leverage insights into borrower’s financial status and aspirations to offer targeted recommendations and optimize marketing strategies, enhancing customer loyalty.
Streamlined Management
Automation creates a well-defined path for the processes to follow. AI tools foster managers to steer activities skillfully by providing them space with trouble-free administration perpetuated by centralized control and eliminating roadblocks.
AI technologies empower lending processes and loan management strategies. AI is revolutionizing every aspect of the loan origination process by unlocking higher levels of efficiency, improved accuracy and enhanced customer experience. AI applications offer the innovation lending organisations seek, to stay ahead of the curve.
