The Essential List: Top Use Cases of AI in Lending That Banks Must Embrace
Banking | 12 min READ
    
AI in Lending – Evolution and Trends
The disruptive digital transformation is sweeping the entire business landscape off its feet, and the change in customer expectations has pushed more and more industries towards faster adoption of advanced technology. Banks and commercial lending institutions are not far from feeling the ripples of this change. They are equally geared to meet these shifts in expectations through well-integrated Artificial Intelligence (AI) and Internet of Things (IoT) solutions.
Preeti Agarwal
Preeti Agarwal

Global Program Director

BFSI

Birlasoft

 
Mckinsey has estimated that AI technologies in global banking can deliver up to $1 trillion of additional value every year. AI-powered solutions are designing innovative propositions and intelligent servicing that can embed without any flaws into the system.
Why Is AI Adoption Catching Up Fast in Lending?
Customers who were previously resisting embracing digital banking have evolved with the change. According to a report by BCG, mobile banking usage surged by 34% between February and June 2020, while there was a 12% decline during banking at branches.
Millennials are keen to go all digital. Incumbent banks are now adopting the vision of “AI-first” to gain a competitive advantage and stay relevant.
The impact of AI technologies is felt more than ever in our lives. From quick translation prompts to conversational interfaces, AI has its implications on transforming the full capability stack.
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This induces core technology and data infrastructure, adds an engagement layer, and powers up decision-making. For better understanding, let us look into some of the relevant use cases:
Top Artificial Intelligence Use Cases In Lending
Top Artificial Intelligence Use Cases In Lending
AI Use Cases In Lending
  1. Cross-Selling for Increased Customer Loyalty and Wallet Share
  2. Credit Worthiness Assessment
  3. Improved Customer Experience With AI
  4. Streamlined Loan Processing With Artificial Intelligence
  5. Data Remediation for Loan Applications
  6. Fraud Monitoring in Processing
  7. Data Scrapping Using OCR From Physical Documents
  8. Intelligent Automation for Middle and Back Office Operations
  9. Machine Vision and NLP For Documentation Scanning
  10. AI-Based Robo Virtual Advisors
  11. Behavioral Analytics
  12. Customer Retention and Upselling
  13. Loan Servicing
  14. Advanced Analytics for Customer Profiling
  15. Alerts and Notifications
  16. Self-service and Knowledge Management
  17. Agility and Team Mobilization
1. Cross-Selling for Increased Customer Loyalty and Wallet Share
Customers today are looking for personalized services. No doubt, it has a significant impact on winning over customer confidence and loyalty. With the growing importance of curated, personalized services, data mining plays is imperative. AI-powered solutions are helping banks and financial lending institutions to access copious amounts of customer data and evaluate them to discern customer behavior.
Services more focused on the need of the customers generate more customer response and engagement. The growing use of customer data and increased computing power to analyze the leads to much more dynamic and insights, enabling customer-centric decision making. Digital channels now allow companies to fine-tune their marketing communication based on these analyses enhancing customer experience and creating more significant cross-selling opportunities.
Customers today rely on multiple vendors for a single service. Customer behavior, if studied adequately, would help businesses gather a chunk of information. Share of wallet would take it a step further, helping in customer retention. AI and automation intervene here and make the process seamless through intelligent data analysis.
2. Credit Worthiness Assessment
Commercial lending depends highly on the credit score of customers. Banks or commercial lending firms do not and will not lend to customers with a poor credit score.
Moving away from the traditional, new-age lenders are moving towards automated credit decisioning systems. Alternate scoring models are put in place to evaluate the creditworthiness and offer loan terms. The entire process of gauging the credit score and finally allowing the loan to the customers is made seamless through AI-based credit scoring.
The credit score of customers is pulled out and evaluated against a set standard. Customer’s eligibility is verified based on the customer bureau data. Up to 3 credit offers are displayed to eligible customers. Loans and credit cards are dispatched once the customer undertakes automated KYC and digital client onboarding, which otherwise can be a cumbersome process if done manually. AI integration makes it time-effective, leading to faster assessment and loan disbursal. This can be one of the many AI integration models.
Customers today expect a faster credit assessment and disbursal procedure. Rapid innovation and investment are happening in the commercial lending sector, serving customers at the right moment.
3. Improved Customer Experience With AI
As AI-enables richer and data-filled customer profiles, banks, and credit lending firms act as financial allies for their account holders. They are not just experts at providing plain vanilla customer products like loans and financial instruments, but they also intend to create a fulfilling customer experience. The application of AI in lending empowers banks to not settle for a B2B role but step up their capabilities towards protection and retention of customer relationships.
For starters, banks are already extracting data out of line items of transactions on current accounts or card statements to convert them into rich customer profiles. They act as the financial companions to the account holders, advising on savings, big-ticket purchases, offering financial literacy guidance, and assisting in critical decision-making. AI is helping them to make this much-needed value addition to its customers’ lives.
These digital lending platforms can leverage data to provide non-financial information to help banks notify specific customers about hot deals and offers that come with a good credit reputation and purchase decisions, providing customers with experiential banking and loan operations.
4. Streamlined Loan Processing With Artificial Intelligence
As digitization sets the stage for automated loan origination and automated loan underwriting, commercial lending can now happen through a few swipes on the mobile phone, just like personal loan applications. However, due to certain regulatory constraints, several lendings are yet to go through this digital transformation, such as mortgage loans and payments. More and more banks are aspiring to automate at least 95% of the manual underwriting decisions.
Furthermore, SME lending has become a digital priority for both traditional banks and fintechs. Both are moving at greater caution when it comes to the digitization of corporate lending. Rather than considering a revamp of the entire customer experience, banks are steadily enhancing the standard processes, for instance, digitization of credit proposal papers, automating annual reviews for improved time and quality.
Some banks enable strategies to help corporate transaction approvers deal with matters at hand, such as automation of low-risk credit renewals, while valuable manual resources are more focused on complicated and high credit risk deals. AI can automate data aggregation to help relationship managers have data and risk-evaluation scores at the tip of their fingers, which considers financial and industry performance, market and sentiment analysis, important news, and external factors. Thus, AI enables the design of a model to help businesses streamline operations.
Top Use Cases of AI in Lending That Banks Must Embrace
5. Data Remediation for Loan Applications
Improving the quality of data is one of the crucial areas AI technologies look into. Most banks and lending institutions have programs prioritizing data measurement, quality analysis, and remediation for issues detected. The first step towards a full-proof remediation process is to discover the concerned areas. Remediation efforts are time-consuming and often create considerable backlogs in the operations. Some banks and institutes have established vast remediation programs with hundreds of dedicated resources, primarily involved in data-scrubbing activities.
AI intervention and technology integration help in setting up better processes for prioritizing and remediating issues at scale. Funds are allocated for setting up AI integrated programs and automating processes. Validated machine learning models are applied to review inputs and develop models based on analytics to ensure faster decisions.
6. Fraud Monitoring in Processing
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.
7. Data Scrapping Using OCR From Physical Documents
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.
8. Intelligent Automation for Middle and Back Office Operations
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.
9. Machine Vision and NLP For Documentation Scanning
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.
10. AI-Based Robo Virtual Advisors
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.
11. Behavioral Analytics
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
12. Customer Retention and Upselling
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.
13. Loan Servicing
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.
14. Advanced Analytics for Customer Profiling
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.
15. Alerts and Notifications
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.
16. Self-service and Knowledge Management
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.
17. Agility and Team Mobilization
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.
AI-powered technology is sooner going to replace the traditional banking and commercial lending space. With the introduction of cloud-based Banking-as-a-Service, machine learning, predictive analytics, IoT, and more coming into the picture, AI-led digital lending would help drive integrated banking experiences for customers worldwide. Additionally, banks would need to augment home-grown models with evolved capabilities to bring a considerable shift towards desired customer experience.
 
 
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