Financial Services

Harnessing AI in Peer-to-Peer Lending: Revolutionizing Credit Accessibility

It’s 2025 and the effects of Artificial Intelligence (AI) are being felt across industries. Consider decentralised financial ecosystems: the integration of AI with peer-to-peer (P2P) lending platforms has fundamentally reshaped the sector on many parameters. Whether with top-line parameters such as credit accessibility or risk assessment, or with bottom-line indicators such as efficiency in operations, lending institutions are leveraging Machine Learning (ML), natural language processing (NLP), and predictive analytics to transact with hitherto untouched populations and boost their balance sheets.

The Evolution of AI-Driven Credit Assessment

AI-driven credit assessment is revolutionising P2P lending solutions in unprecedented ways. Here, we examine two of the key evolutions.

Redefining Creditworthiness Through Alternative Data

Traditional credit scoring models that prioritise historical financial records typically exclude individuals and small businesses with limited credit histories, that is those whose ‘character’ they are unable to determine. AI disrupts this paradigm by analysing alternative data points to build holistic borrower profiles—data points such as mobile phone usage patterns, online shopping behaviors, and social media interactions. For instance, platforms like Zylo P2P Investment use ML algorithms to evaluate cash flow consistency from gig economy transactions, enabling lenders to extend credit to freelancers and informal-sector workers previously deemed "unscorable". This shift has reduced credit approval times from weeks to minutes while expanding access to $190 billion in global P2P lending markets.

With these new capabilities, lenders can make personalisation and financial inclusion a goal of their lending programs, offering hyperpersonalised loan products to diverse demographies. Gig workers might receive loans with flexible repayment schedules aligned with irregular income streams, while small businesses access invoice-backed financing at competitive rates. In developing economies, where 1.7 billion adults lack access to formal banking, AI-powered P2P platforms analyse alternative data like mobile money transactions and agricultural yield forecasts to extend microloans. A 2025 pilot in Kenya used satellite imagery and AI to assess farmers’ creditworthiness, resulting in a 63% approval rate for unbanked applicants.

Enhancing Predictive Accuracy with Machine Learning

Repayment outcomes are one of the biggest risk factors with running a profitable lending institution. Accurately predicting what borrower profiles may repay is a tremendously beneficial endeavor to the bottomline. AI is at hand here too. AI-powered risk assessment models outperform traditional methods by identifying subtle correlations between borrower behaviors and repayment outcomes. For example, recurrent neural networks (RNNs) analyse sequential data—such as monthly utility payments—to predict delinquency risks dynamically. These models continuously refine their predictions using real-time data, allowing platforms to adjust interest rates or loan terms proactively. A 2025 study demonstrated that AI-catalysed platforms reduced default rates by as much as 27% compared to conventional systems. This was done mainly by flagging high-risk applicants during underwriting of loans.

The reasons for this evolution are not hard to find. First off, lending platforms and finance corporations are able to better assess the profiles of loan applicants.

Risk mitigation and fraud detection have improved by leaps and bounds in recent years, again driven by AI solutions. Real-time fraud prevention mechanisms such as deep learning models flag suspicious loan applications, and monitor transaction velocities and IP geolocation mismatches. AI also enables dynamic risk assessments that take into account borrower-specific life events such as layoffs or industry downturns.

Next, lending firms are able to extract new efficiencies in their ops through automation. Loan origination and underwriting are being streamlined today, courtesy AI. AI-powered tech automates labor-intensive tasks such as document verification, income validation, and compliance checks.

Lenders can also opt for investor-centric portfolio optimisation which helps them analyse risk-return profiles across thousands of loans. Clustering algorithms categorise borrowers by risk tiers, enabling automated portfolio diversification. Predictive analytics forecast interest rate trends, guiding investors to allocate funds toward high-yield opportunities.

Not without challenges

It must be noted that challenges and ethical considerations in deploying these AI-driven systems are significant. Poorly trained AI models may perpetuate the very biases and disparities new-age lenders seek to mitigate. For instance, algorithms correlating ZIP codes with default risks could disproportionately deny loans to marginalised communities.

The use of non-traditional data also raises privacy concerns, particularly under GDPR and CCPA regulations. Industry mavens are tackling these challenges by developing anonymisation techniques like federated learning, where models train on decentralised data without accessing raw information.

Sunny skies ahead, for all

What are the future trajectories in this market? Business leaders are looking at technologies like AI crypto agents and Decentralized Finance (DeFi): exploring integrations with blockchain via autonomous crypto agents to negotiate loan terms across decentralised platforms. These agents use reinforcement learning to optimise interest rates based on real-time liquidity pools, enhancing market efficiency. It’s projected that by 2026, 60% of P2P platforms will adopt such systems, thereby reducing intermediation costs by 50%.

The integration of AI into P2P lending has leveled the playing field for credit access, thereby helping millions to bypass traditional financial gatekeepers. However, realising AI’s full potential demands rigorous ethical frameworks to prevent bias and to protect user privacy. The next frontier lies in creating self-regulating, globally accessible lending markets that transcend geographical and socioeconomic barriers.

How Infosys BPM can help

Infosys BPM offers an exhaustive suite of services to enterprises in the lending sector, designed to streamline operations, reduce costs and improve business performance. From Market Research to Account Maintenance and Default Management, Infosys BPM’s lending practice includes over 12000+ financing experts across the world who work for 100+ clients. Our core offerings include end-to-end integrated IT and business outsourcing services across the lending value chain.