Consumer lending has undergone tremendous changes in the last two decades. From the only borrowing option being from banks and personal lenders to the rise of FinTech organisations and online lenders and now AI-ML-enabled lending, things have changed drastically.
Banks and online borrowing platforms face innumerable challenges during the entire lending process from sanctioning loans to improving customer care and complying with regulatory organisations such as FCA and loan recovery.
Safety is the number one concern for many lenders because online fraud has become extremely sophisticated. With an early warning system in place, lenders can land in financial loss and suffer irrevocable reputation damage. Artificial intelligence, machine learning (ML) and predictive analytics are helping deter scammers and safeguard the assets of lending platforms. Let’s look into how online lending has become safer with artificial intelligence.
Better Fraud Detection
The UK Finance reported that in 2022 alone, £1.2 billion was stolen through authorised and unauthorised frauds, which has become a major challenge for online lenders and FinTech organisations. However, with the use of AI, this can be deterred to a huge extent.
Artificial intelligence analyses real-time data to detect any usual activities and flag discrepancies. AI apps undertake extensive background checks on borrowers to understand their creditworthiness or any sign of identity theft. This allows for early detection of scams and helps lenders save their assets from significant losses.
If they identify any discrepancies, AI will flag and alert associated stakeholders, banks and consumers alike. As cyber criminals continue to target financial institutions and borrowers alike, using advanced AI and ML-integrated applications goes a long way to avoid financial fraud.
Checking for Borrower’s Credit Worthiness
Credit checks help banks and lending platforms better judge a potential borrower’s creditworthiness. Machine learning algorithms can look into the historical data of an individual and their spending habits to understand if they can potentially default on their loan.
These applications will not consider traditional data (income statement, credit history and employment records) and non-traditional data (social media activity and other online behaviour). But, this alternative approach is arguably better as it opens up borrowing opportunities to those who may have poor or no credit history. This inclusive, data-enabled approach helps those with bad credit payday loans or students who lack a proper credit score. If you seek more information about credit checks, you can always ask the bank for more information.
Personalisation For Better Lending Terms
Customisation is the key to a great borrowing experience for both the lender and borrower. AI-powered models analyse the loan seekers’ financial behaviour and preferences and accordingly suggest the loan amount, terms and interest rates. This helps banks safeguard their assets without compromising on customer service.
Personalisation will increase the acceptance rate and provide better customer retention. With there being tremendous competition among lenders and FinTech platforms to acquire and retain customers, AI can be strategically used as an advantage.
AI-enabled customisation allows online lenders to offer competitive rates based on market conditions. This helps lenders to stay profitable without compromising on client satisfaction.
Improved Compliance of Lending Platforms With Regulatory Bodies
One of the biggest challenges that is faced by online lending authorities is staying compliant with regulatory rules. AI-integrated applications help lenders make sure they are compliant with the loan application process, which includes checking for necessary documentation, verifying if the borrower’s information is correct and making sure all other legal criteria has been met.
Compliance failure can also open up opportunities for fraud such as identity theft and money laundering. An online authority that is found to not meet the standards prescribed under FCA can lead to reputational damage and heavy penalties.
Banks and other financial institutions safeguard personally identifiable information (PII) as this will help maintain customer trust. Data security and advanced cybersecurity are key to ensuring the confidentiality of customer information and banking data
Wrapping Up
AI-powered online lending has improved the safety and security of both lenders and borrowers alike. Artificial intelligence algorithms are enhancing the efficiency and accuracy of the lending process, and predictive analytics assists banks in removing the degree of uncertainty from the lending process. This is hugely beneficial as it can help to minimise the chance of bad loans.
Some of the other benefits of AI integration in online lending include better scalability, improved decision-making and early fraud detection. A large portion of online lending has become safer in the age of AI, which in turn has made it safer for many consumers. Other benefits include faster approvals, better accessibility and personalisation.
While AI can open up more opportunities for threats, such as deep faking identities and sophisticated frauds, when used with caution, there are many advantages for lenders and consumers.
