Top Use Cases of AI in Lending For Banks to Embrace

Disruptive digital innovations and most businesses moving online have led to a considerable rise in cybercrimes. Identity thefts, phishing scams, ransomware attacks, theft of data are rampant.

Identity verification of loan borrowers can be faster and more secured with AI and blockchain technology. Smart contracts could monitor and regulate a loan throughout its entire lifecycle. It helps in eliminating the chances of defaults, delayed reporting, and unreported loans. AI enables solutions that give a shared control view of the historical records, authentication of financial information and data. Shared access and consensus assure limited scope of fraudulent activities, efficient governance, and underwriting process transparency.

While banks and companies are going “AI-first,” a sustainable and sturdy technological backbone needs to be in place. In a bid to build technology overnight, poor security and frauds pose the most significant threats making all the information vulnerable. Therefore, adequate security testing is a must before innovations are rolled out and made available for the greater good.

Output Character Recognition (OCR) is a specialized solution to decode the characters of a text within the images. It converts texts containing images into characters that computers can easily perceive to decide the future course of action.

Banks and commercial lending firms can use OCR to extract data from cheques to record account information, handwritten amount, and the signature for authorization. Loan applications contain an array of documents and information. AI technologies help in the seamless scrapping of this data and information from all such physical documents. Modern deep machine learning can capture further data points using OCR to process better outcomes helping in faster data record and maintenance.

Though slower, most banks have started automating their systems across business functions. Especially in the credit and loan operations processes, several stakeholders need to be aligned and constantly remain in sync over entire operations and functions. Legacy IT systems, a lack of trust automation, insufficient cooperation between businesses and tech have already delayed the deployment of intelligent automation. However, banks are steadily gearing up to set up automated lending systems across the middle and back-office operations.

Allocating people with the necessary skills, pilot testing systems before going live, and considering the entire hierarchy while devising and deploying solutions, AI carefully designs a planned approach realized through intelligent automation.

Commercial lending involves massive documentation and making sense of these documents. Much needed for keeping a tab on the lending and borrower's details, innovative AI solutions today use business machine learning problem mapping to extract the content of these documents. With intelligent document indexing, institutes can easily integrate an AI-powered framework for information processing.

Deep learning algorithms and architectures have made impressive advances with Natural Language Processing (NLP) techniques making notable contributions. Name Entity Recognition, topic modeling, and sentiment analysis are breakthrough innovations that have eclipsed the traditional methods. NLP also has a significant role to play in the automation of compliance requirements. These advanced solutions have reduced manual and tiring labor and value addition for the lending firms.

One of the dramatic innovations of AI, Robo-Advisors, is to devise potential solutions to drive complicated processes. These Robo-advisors could support customers with financial and support guidance. The algorithms could advise the best loan approval, set reminders, provide complete customer services to generate a solid financial portfolio for customers.

The intelligent algorithms perform various functions and, when integrated with the overall technology stack, would be helpful in decision making. Without the interference of humans, they can address and take up straightforward user queries reducing the load on the contact centers.

Several banks and fintech firms have developed operational instruments to process transactions from primary operating accounts, classifying them into detailed revenue and expense items. Using advanced behavioral analytics, banks and institutes can generate simplified financial statements, affordability ratios, real-time customer and supplier concentration analysis. These transactional data offer updated insights and substantially richer and pragmatic data solutions, building subsequent, gradual data improvement models. A standard behavioral analytics model helps in innovating a unique customer experience and enhanced lending process

Ambitious data integration and user behavior analytics drive AI-powered lending institutions to identify and create upsell opportunities for customers. However, implementation and integration depend on time. However, to stay above the crowd, upselling opportunities make way for retaining customers.

Latest AI technology redefines interactions between customers and banks. Facilitated by reduced cost and data storage, increased access, and connectivity, these technologies enable banks to introduce customers to experiential banking that further boosts revenue and unfolds newer opportunities.

AI-powered solutions efficiently handle servicing of loans and are the first interaction between customers and lending organizations. Interactive AI chatbots help in addressing customer concerns quickly. Capabilities are devised to send timely monthly statements, EMI collection, maintaining records of advance payments, withdrawals. AI can devise end-to-end solutions to record customer's requests, tax collection, and insurance payments, remitting funds to the note holder, and making a note of any delinquencies.

In this digital age, customer profiling is the best way to understand your customers' interests, behavior, purchase decisions, and club them into segments of similar data attributes. AI empowers banks and lending institutions to analyze specific attributes of their borrowers, studying the gender, income levels, historical data, age, location, years of service, and group them in clusters with similar attributes. This enhances customer understanding based on which businesses curate services.

Customer profiling helps banks and financial institutes in several ways. User data maintenance is more accessible and convenient for banks to spot customer behavior and activities. This helps lending institutions keep a tab on the defaults, timely payments, missed payments, and more.

Once AI technology is integrated within the overall system of commercial lending, customer communication shows an improvement. Timely reminders are sent to the borrowers for due dates, missed payments, change in dates, and premium amounts. Customers are updated with the latest offers, new products, and any latest collaborations that help them decide.

Customers these days aspire to know everything before making a purchase. AI-driven operations model ensures that customers are updated with all the whereabouts before opting for a loan. This helps them in making informed decisions. Banks and lending institutions can be fully transparent and compliant to avoid falling into unnecessary trouble in the future.

Significant technological advancements in a traditional setup often create huge gaps within an organization. AI integration addresses all these gaps and clarifies roles and responsibilities by seemingly educating everyone in the organization. Enhance board reporting from a granular level to upwards. Evaluate internal stakeholders through self-assessment and addressing the needs through a well-developed action plan.