AI in Lending
The strategic deployment of AI in lending can bring substantial benefits. Learn more about how AI tools are transforming the lending ecosystem.
With margins under pressure, credit quality deteriorating, and customers demanding fast, digital services, extracting more value from commercial lending has never been more critical. Technology has already made significant strides in automating, integrating, and consolidating previously expensive complex processes. How can artificial intelligence (AI) take commercial lending digitization to the next level?
The short answer is that AI can use data far better and faster than other technologies or humans.
AI integrations have the potential to significantly lower closing costs by streamlining and improving the efficiency of the lending process. Lenders and borrowers will benefit as AI becomes more integrated with home loan tools like mortgage calculation software or underwriting systems.
AI in Lending Today
Lending institutions are already using automated processes. Through pre-written scripts, robotic process automation (RPA) assists in sorting through mountains of complex paperwork. When RPA automatically fills in the appropriate data in the blank fields of required forms, it helps reduce manual errors and is a typical application of AI in lending.
ML and AI are developing that fundamental automation. Using AI algorithms, credit decisions can be made more quickly and accurately by sorting through documents and, equally important, contextualizing digitized information. As a result, professionals can focus on areas where human decision-making has a more significant impact while AI takes care of mind-numbing tasks.
AI frequently only provides recommendations; humans still make the final choice. The AI-human-in-the-loop combination is more productive by enhancing human abilities, with each component utilizing its strengths.
Why is AI Adoption Catching Up Fast in Lending?
Customers who had previously been reluctant to adopt digital banking have changed along with it. Millennials want to live entirely digitally. Established banks are now embracing the "AI-first" philosophy to maintain their relevance and gain a competitive advantage.
Our lives are more affected by AI technologies than ever before. AI will impact the entire capability stack, from instant translation prompts to conversational interfaces. Core technological and data infrastructure are introduced, an engagement layer is added, and decision-making is boosted. Let's examine some of the relevant trends of AI in lending for a better understanding.
Trends and Adoption of AI in Lending
Following are some of the main trends of AI in lending.
Cost Optimization and Scalability for Growth
Given the changing circumstances, digital lending is spreading among banks and lending organizations. Artificial intelligence (AI) and machine learning (ML) are significantly improved compared to conventional statistical models. This innovation takes the lead when it comes to maintaining transparency and performance.
The overall productivity is increased by reducing manual intervention and making AI/ML models more sensitive to data change and outliers.
Lending Cycle Time Reduction
Through intelligent AI technology, lenders can shorten the time it takes to process a loan from weeks to hours. The initial stages of loan processing require significant documentation work, which takes a lot of time. Artificial intelligence in loan processing reduces the overall time spent on tedious work processes by enabling automation throughout the lending cycle.
Data Availability for Better Credit Decisioning
Assigning credit ratings to businesses and (or) individuals requires a significant amount of knowledge, effort, and time, from evaluating credit scores to determining eligibility to allocating the proper amount to the borrowers.
The time it takes to assess a company's financial situation is reduced by automated credit decisioning systems made possible by data-driven AI solutions. Examining a more significant number of data points for a shorter period and producing quicker credit scores enables closer monitoring of its activities and creditworthiness.
Inability to Handle High Loan Application Volume
AI and ML models can help banks and lending organizations improve rule-based underwriting. Accuracy is enhanced by this advancement, especially when processing loan applications in bulk. Credit demands have increased as a result of the growing uncertainty.
Fintech companies and banks evaluate customer information and creditworthiness using these intelligent technology solutions. Banks can continuously improve these models by carefully monitoring the results. Underwriting automation, credit decisioning process implementation, and digital lending help meet unexpected demand surges, improving overall system resiliency.
Shift in AI Adoption in Large Banks
The digital transformation has compelled more established, regulated entities to use AI and ML models to reduce credit risk for banks. These models provide a sophisticated framework for managing the entire lending process, give a clearer picture of the ratings and financial activities, and increase efficiency in addition to helping to assess real credit risk.
Loan Processing Using Digital Data
Automated AI lending and loan processing replaces manual processes in the highly connected digital nexus. Similar systems' manual nature would result in significantly higher costs for lenders, reflected in high employee costs, lost time, and subpar customer service.
Fintech firms adopted AI for loan processing, ensuring smooth loan approval and disbursement.
Business Drivers for AI Adoption in Lending
Let's look at some of the business drivers for AI adoption in lending
Need for Better Customer Experience
Using AI in lending automation is revolutionizing essential business procedures. Customers want as much involvement and total transparency as possible. Customer-centric personalized interactions are now possible for better engagement as the world becomes more digital and new technology solutions shape businesses.
The lending industry frequently deals with fraud and identity theft. This problem can be addressed at the source using AI tools for fraud detection and elimination to ensure safer practices. Lending institutions can provide better customer services by modernizing and streamlining laborious admin tasks, automating contact centers, and obtaining a 360-degree view of the customer. Customers can now access their transactions, payments, and history for essential financial data thanks to streamlined processes and increased trust.
Additionally, businesses can better understand customer needs by using data intelligence and advanced analytics to create carefully considered solutions. Customer experience will be amplified when multiple factors are combined, which is what is required right now.
Client Acquisition and Engagement for High Profitability
A seamless client onboarding process is the first step in making a great first impression. A potential lead connects with the sales team to start the process. It goes through several steps to compile crucial client data and the various regulatory, legal, and due diligence checks. The whole procedure is drawn out and tedious.
A McKinsey report suggested that banks could boost their profits by 40% by implementing digitization. All regulatory procedures and requirements are handled by automated KYC (Know Your Customer) verification, which makes the process simpler and faster. AI offers countless opportunities for easier and less risky client onboarding, from digital signatures to e-verification to quicker query resolution.
In the lending ecosystem, AI and automation have been investigated in terms of profitability, efficiency, cost, and end-user experience. Customers' footprints on the company's social networks and online presence have changed how credit decisions are made. AI-based decisioning systems produce data from tens of thousands of customer characteristics and numerous data points, giving customers exactly what they need and reducing the amount of time that needs to be spent by humans.
According to estimates, AI-based credit scoring lowers loan origination costs by 40%, improving compliance and scalability. AI-based decisioning also reduces maintenance expenses and increases banks' capacity to serve all first-time borrowers.
Automation of Credit Decisioning for Better Risk Management
The entire financial value chain is being rewritten using AI-based automated credit decisioning systems. These technological developments give traditional underwriting operations a competitive advantage by providing 100 times more data points to evaluate risk management. Artificial neural networks' ability to analyze and rank linear and interactive patterns has the potential to revolutionize many fields.
AI and ML will soon assist banks in decoding risk posture, whetting their appetite, and exploring consistent movement towards exploring new levels of profitability by utilizing the power of these next-gen solutions.
Benefits of AI in Lending
"Failure is only the opportunity to begin again, only more wisely this time." - Henry Ford once said.
We can advance more fairly and intelligently if we acknowledge the initial difficulties with AI technology. Financial service providers can more intelligently adapt solutions to target and lessen instances of bias in lending by being aware of the challenges.
Here are a few ways AI can be modified to promote more equitable lending:
Allowing Access to a Broader Range of Financial Products
The methods used to determine risk and evaluate data previously had a limit on the range of lending products available on the market. Businesses can expand this list and improve their offerings to meet customer needs with big data. This provides the opportunity to tailor services and repayment more precisely while lowering the risk for the lender for those who have trouble obtaining credit.
Automate Internal Services to Allow More Client Focus
AI enables businesses to automate processes to make them faster than ever before for everything from filling out application forms to managing accounts to organizing repayments and calculating risk. This enables financial institutions to give customers their full attention. This can entail anything from offering advice on the best loans available for the specific client to lowering the risk to the lender if the client's data is incorrect.
Streamlining Application Process to Make it More Accessible
For some people from minority backgrounds, the loan application process becomes a barrier. Inaccessible language and endless forms make things difficult, and some give up before even starting. By streamlining the application process, AI-based chatbots can make the process more accessible.
Creating Optimization in Credit Scoring
Although credit scoring shouldn't be a Black and white process, that doesn't mean it needs to be complicated. When used correctly and with care, AI technology can help lending companies improve their credit scoring procedures, create customized loans, and provide scoring free of human biases. This might entail improving systems that deal with default risk, adding personalized repayment terms, or adjusting for an individual's or company's risk.
Companies Using AI in Lending
Here are some of the companies using AI in lending.
Upstart - Full Automation and AI-Determined Creditworthiness
An influential start-up, Upstart uses AI to speed up the loan application process and assess creditworthiness. Two of its co-founders were former Google employees. Upstart began by focusing on young adults with little credit history. In addition to traditional FICO scores and credit history, Upstart considers education, SAT scores, GPA, a field of study, and job history to predict an individual's creditworthiness using machine learning.
Upstart's main objective is to automate the loan application process using contemporary data science. They assert that the number of loans that can be fully automated has increased quickly. While other companies have automated some of the data entry, paperwork processing, and primary information verification, before being approved, most loan applications are still examined by a human underwriter—a person who verifies that the information is correct or that the loan makes sense. Upstart is a hybrid lender that makes some loans directly and facilitates others. It has also begun providing its technology to other businesses via software-as-a-service.
Personetic - Loan Repayment
It is a cognitive banking company that provides artificial intelligence (AI) applications to central banks (such as the Royal Bank of Canada and Ally Bank). Personetics Act, a recently unveiled application from the company, is a tool to assist individuals in saving money. They employ the same basic technology to assist individuals in repaying their student loans more quickly.
They claim that their system employs machine learning to analyze individuals' financial habits to determine whether they can repay their student loans more quickly. The system can then automatically suggest how much more individuals should contribute.
Lenddo - Digital Footprint Analysis
Lenddo is one startup that actively mines vast sources of alternative data using cutting-edge machine learning to forecast a person's creditworthiness. The company was founded in 2011 and focused on emerging markets where rising middle-class populations frequently lack traditional credit histories or bank accounts. They claim that 5 million people have received loans through their partners due to their system's ability to evaluate their creditworthiness.
By having people download their app, Lenddo examines a potential applicant's entire digital footprint to determine their creditworthiness. According to them, it considers over 12,000 variables, including social media account usage, internet browsing, geolocation data, and other smartphone information. This data is converted into a credit score by their machine learning algorithm, which banks and other lenders can use.
The entire business landscape is being upended by disruptive digital transformation, and the shift in customer expectations is pressuring many sectors to adopt cutting-edge technology more quickly.
Technologies like AI assist lending institutions in automating their business processes and being efficient due to digitization. The loan application process is made simpler and less risky by using AI-based solutions. Customers can now apply for loans with just a few clicks, and lenders can process those applications more quickly thanks to cutting-edge software.
AI-powered technology will eventually replace traditional banking. With the introduction of cloud-based Banking-as-a-Service, predictive analytics, machine learning, IoT, and other technologies, AI-led digital lending would help drive integrated banking experiences for customers worldwide.