IT-driven process modernisation has seen evident results in the financial services sector. AI and ML applications are gaining attention from business leaders following the results of new-age applications delivered by fintech companies. These applications make operations simpler, fulfilling the expectations of multiple stakeholders. They are capable of translating large, complex, dynamic datasets into simple actionable insights for quick decisioning. AI-powered data analytics resources are equipped to streamline workflows and enhance organizational efficiency. AI is already revolutionizing in every sector. Loan management in Banking and financial services stands to gain the most from AI.
Traditional loan origination workflows rely on tedious manual processes, These are time-consuming and prone to human error. For future-oriented organizations, such workflows only impede their growth as they fall short of tackling rapidly evolving business demands and competition. AI/ML-powered fintech tools can be game-changing for businesses with their intelligent automation of loan origination processes. There has been a revolution in the lending industry with loan management software solutions reinventing strategies for organizations to enhance their efficiency and aim for a larger market share.
Data Extraction with AI/ML
The credit underwriting process begins with the collection of identity proofs and financial information as attachments to the loan application. Identity records attest to customers’ personal and demographic information. Financial documents collected differ based on the purpose of the loan, whether it is for personal or business purposes. Passbooks, bank statements, and income tax returns are collected for creditworthiness computations. Payslips, Balance sheets, P&L and cashflow statements apply to respective cases. These are records of the applicant’s financial stature and creditworthiness. Extracting and classifying the data from these documents is challenging. The documents come in varying formats and styles with non-standard terminology, often as poor-quality scanned images. Underwriters put in hours of effort to uncover relevant information.
With AI/ML-powered loan management software, the data processing from these documents becomes simpler and faster with higher accuracy. Modern solutions can comprehend data presented in diverse layouts. They recognize descriptions with precision and their respective values. Purpose- built ML algorithms allow automated data extraction from low-quality scanned or photographed images.
Data Verification and Authentication
The verification of information in the documents is pivotal to safe lending decisions. Identifying doctored or incorrect information can help detect credit risks early on. This warrants diligent checking and cross-verification of the data across lengthy financial statements and annual reports. Manual verification is labour- intensive and exhausting. The accuracy of the results from such a manual process is questionable due to the limitations of human efforts.
AI-powered tools for data verification enable reliable and quick validation of information. For instance, bank statement verification software can detect discrepancies and inconsistencies in transaction data empowering underwriters to make informed decisions. AI solutions offer measurable, consistent results, improving their performance with time. The rigorous evaluation of information ensures minimized risks and profitable lending decisions.
Leveraging AI/ML as a Data Analytics Resource
Data analytics is a crucial part of AI-powered loan management systems. It equips lenders with the ability to acquire valuable insights into the borrower’s financial status with unparalleled precision. Modern AI tools approach data from a holistic perspective, beyond calculating credit scores. They can dissect data points from different sources, revealing indicators representative of customers’ financial behavior through income patterns, aspirations and liabilities. With predictive analytics, lenders can foresee how the applicants’ financial status will change with time. AI also allows for customizations for specific users or cases. They can contrive recommendations for the best time to promote certain products to specific customers.
Data-backed Credit Decisions
All lending organizations rely on a credit score to approve loan applications, with their own specific processes to arrive at those scores. The credit score is a measure of the borrower’s repayment capability. The value is computed using applicant’s revenue and expense details submitted in the documents. However, a score is often insufficient to accurately reflect the customers’ financial well-being.
AI-powered loan management software uses broader and smarter analysis for credit score computations. They prove to be more reliable and consistent as they are based on a comprehensive analysis of bank statements and other financial documents.
Conceptually, modern AI tools can autonomously approve or reject loan applications based on the data provided. However, real-world deployments opt for a human-in-the-loop approach. In this case, AI provides inputs and guidance, but it takes an experienced underwriter to make the final decision, thereby balancing robust machine intelligence and sound human judgment.
Streamlining of Processes
Today’s market pressures and rising customer expectations often push business leaders to prioritize operational efficiency even as margins get squeezed. Processes are accepted if they are scalable to adapt to the rapidly changing competitive landscape. Manual tasks often fail the scalability test.
AI-based loan management empowers organizations to transform their underwriting workflows. The dependency on human skill and efforts significantly reduces with rules-driven, systematic and accurate processes. These accelerate every stage in the loan cycle from data extraction to credit score calculation and loan approval decisions. Loan cycles require diligence and accuracy at all stages beginning with data extraction and classification, credit score calculation to final credit decisions. Lenders can minimize the chances of high-cost lending decisions through accurate and proactive assessment of risk factors. AI-powered loan management systems are centralized for managers to be in complete control of the workflow, with intuitive dashboards and real-time reports.
Improving Customer Experience
Two important factors that displease borrowers are: delayed approvals and the lack of transparency in the process. AI-driven loan management assistance solves both of these factors. It facilitates a streamlined underwriting workflow to make loan decisions faster. It addresses transparency with a customer-friendly interface that updates the borrowers on their application.
Beyond automation of routine tasks, AI can act as a friendly cohort to underwriters, by providing valuable advice throughout the loan origination cycle. Organizations can benefit from intelligence, to address applicants’ concerns comprehensively with insight. AI can boost pre-sales or post-sales support, with NLP-powered chatbots that offer 24/7 assistance with responses to common queries. They can guide borrowers through the application process and KYC documentation needs.
AI as a data analytics resource presents a capability to customize offerings and improve customer relationship management. Intelligent processing gives an understanding of borrower’s expectations with the closest precision, for lenders to tailor their products and services based on customer-specific requirements. Such a personalized approach to lending fosters trust and loyalty, with delighted customers, paving the way for mutually rewarding engagements.
