# # # #

Five ways AI will Assist the Credit Origination Process

By Murad Baig, Chief Innovation Officer and Chief Product Officer OTOZ, NETSOL Technologies, Inc.

Five ways AI will Assist the Credit Origination Process

By Murad Baig, Chief Innovation Officer and Chief Product Officer OTOZ, NETSOL Technologies, Inc. on 11-12-2019

Credit Origination Processes are shifting across the financial services sector. Artificial intelligence, machine learning, risk analytics and online lending processes have transformed lending. Financial services firms are pursuing innovative ways to improve lending operations. It is one thing to change long-standing processes when hundreds of thousands of dollars are involved, the risk would be substantial. But, implementing modern technologies and processes for smaller, lower-risk loans is a more attractive option. As such, the move toward emerging lending models is particularly evident in such sectors as auto finance.

Creating value from smaller loans depends on fast, efficient processes. AI and similar technologies are becoming critical as a result. A US$30,000 loan spread over four years will only be so valuable if it takes the team hours of work with multiple employees to process it. The cost of analysing, approving and underlying the loan becomes sufficient to undermine the value potential of the financing opportunity. When more tasks are automated with AI, financial services firms can offer a wider range of loan types without sacrificing profitability.

The Rise of AI in Financial Services

The banking, financial services and insurance (BSFI) industries are adopting AI at a breakneck pace. A Market Study Report found that the value of the AI market for BFSI use cases will expand at a compound annual growth rate of 30% from 2018 through 2024. AI is gaining momentum so quickly as firms leverage AI to:

  • analyse customer behaviours to create more personalised experiences;
  • leverage modern analytics capabilities to improve fraud detection; and
  • create robust, intelligent algorithms to bolster credit decisions.

The study found that North America held a 50% global market share for AI solutions in the BFSI sector in 2017. However, the Asia-Pacific market is growing quickly, and AI adoption is escalating in that market. AI market value will rise at a 40% CAGR from 2018 through 2024 in the Asia-Pacific region. By 2030, China is widely expected to be a world leader in the AI sector.

To a certain degree, AI is gaining momentum due to its ability to inform immediate operations. However, the technology is also becoming popular for predictive analytics. AI empowers lenders to better anticipate market trends and consumer activities. For example, predictive analytics can help OEMs identify when a customer is more likely to want to make a car purchase so they can optimise their outreach efforts. AI is opening-up new opportunities in lending, and it is doing so largely because of how it transforms the credit assessment and evaluation process. This creates opportunity in two key areas:

1. Meeting Customer Demands:

A Credit Union National Association report explained that consumer purchasing habits for vehicles have skewed online. As such, they expect similar digital convenience from the lending process, something that AI-backed online lending systems make possible.

2. Broadening Lending Opportunities:

Process efficiency in the loan process changes the risk threshold. If it is not as expensive to process a loan, then the revenue generated from funding does not need to be as great to justify the risk. As such, lenders can diversify their lending portfolio to avoid situations where declines in one or two markets seriously hurt their business.

AI creates these opportunities to innovate and position lenders to modernise their lending processes through stronger credit assessment and evaluation. AI makes this possible in five ways:

1. Innovative Credit Scoring Models.

Traditional credit scoring models use fairly standardised, rigid algorithms to calculate a person's reliability with credit. Historically, this method worked for a few reasons:

  • Data sharing across various financial systems was severely limited as service providers used closed off networks and even paper-based processes to manage credit and transaction histories.
  • Information pertaining to spending habits and its influence on a consumer's ability to handle credit is more widely available through alternative databases that are available to financial service providers.
  • Deeper access to cash flow analysis gives lenders a larger data set they can use to more accurately evaluate risk.
  • These capabilities add up to create a situation in which credit decision-making systems have more robust data to pull from when considering credit scoring. For example, today's digital lending trend gives financial institutions access to a wide range of historic borrower data. As such, they can use AI systems to parse through localised data to identify any regional trends influencing credit viability. That analysis can then be incorporated into credit risk modeling to accompany traditional credit scores based on the unique dynamics of a local market. In practice, credit scoring is highly dependent on having large amounts of data available to provide an accurate prediction of an individual's ability to handle credit. Access to data has long been limited in the industry. Because of this, credit scoring models have had to be extremely rigid to ensure an equitable lending environment. However, the marketplace has been moving faster than credit scoring models for a while now, creating an environment in which blind spots in traditional scoring methods limit lenders and borrowers alike. AI changes this, something that is only possible because of the amount of data being generated and shared within the industry. AI systems can parse the large quantities of information available to lenders and use that data to identify gaps in credit scoring. From there, financial institutions can analyse recommendations from AI systems alongside what they are using to update and optimise their scoring methods on an ongoing basis. Instead of using a few legacy credit scoring tools that rarely change, lenders can apply variable models to different loan types based on their specific needs.

    2. Automated Decision-Making Processes.

    AI has a great deal of potential to automate lending processes and create opportunities for greater efficiency when analysing loan applications. Most traditional lenders feature some form of complex, manual process they must complete in order to approve a loan application, manage underwriting and obtain funds. Breaking out of highly efficient, digital work methods to handle manual data gathering and analysis gets in the way of efficient credit decisioning. AI solutions that support decision-making can:

    • aggregate data from third-party databases and organise it into key metrics for credit decision-making;
    • analyse historic lending data to identify patterns that point to risk or opportunity based on a loan applicant's transaction data;
    • use data analysis and digital forensics capabilities to support identity verification and simplify fraud prevention; and
    • automate regulatory compliance by organising data into relevant fields and accurately tracking user processes to avoid manual data entry and reporting.
    • While AI creates significant potential for decision-making automation, it is vital to blend automation with human-focused processes to optimise the value created during decisioning. A FICO report explained that end-to-end automation with AI can actually increase risk as the AI system constantly evolves based on the data it is receiving. As such, the AI can develop bias without human workers noticing. Relying on AI can also make it difficult to adequately understand why AI software chooses to reject a loan. This may sound alarming, but it is not a direct indictment of AI technology. Instead, it is a statement of the technology's immediate limitations and the importance of countering those flaws with strategic use of alternative digital technologies.

      Over time, the potential visibility gaps created by AI will disappear. In the meantime, AI is not applicable for end-to-end anyway. To best optimise AI use for decision-making, lenders need to use digital lending tools that give users access to vital data that informs choices. In some cases, the best option is to have an AI perform analysis and show the work to a human who can analyse the process and make a decision. In others, organisations will automate the decision-making, but use ongoing reporting and analysis of loan decisions to ensure the choices made by AI systems align with the firm's standards. With either options, digital reporting systems allow firms to counter the risk currently involved in AI use while the technology matures. Consumer lenders need to provide transparency into why loans are declined and protect against discrimination, FICO explained. Blending AI with checkpoints with human workers at key points in the decision-making process can empower lenders to accelerate and improve their operations.

      3. Intelligent Loan Product Matching.

      Lenders have an opportunity to optimise their ROI situation by matching loan products with customers. Recognising the size of a loan that a customer can afford within a certain risk threshold and approving loans accordingly is useful and the basis for traditional loans. However, lenders also have specialised products and services at their disposal. These can not only be more valuable for the bank, but also improve the customer experience and create brand loyalty. Imagine a customer applies for a US$50,000 loan. A lender can perform analysis without AI, look at customer bank records, and determine whether they can afford the loan at a certain rate. The rate is a little higher because of risk in the customer's history. As a result, the lender is left wary of losing the client to a less riskaverse competitor.

      When using AI, lenders can analyse alternative data sources to assess customer financial histories and identify opportunities for loan optimisation. That analysis may reveal, for example, that the loan applicant is actually a candidate for a specialised product the lender provides but had not provided the necessary information for the firm to recognise the opportunity manually. Because of AI's ability to analyse data that is too cumbersome for human workers, you can seize an opportunity. Borrowers today are looking for better experiences. They do not necessarily want to analyse every loan option and apply accordingly. If you can automate that analysis during the credit scoring and decisioning process, you can create a stronger experience.

      4. Simplified Assessments for Unconventional Customers.

      Reaching individuals who have bad credit, are credit invisible or otherwise lack access to traditional financial services can provide lenders with an opportunity to capitalise on an otherwise untapped market. AI makes it possible to do this with lower risk. An Entrepreneur report explained that third-party databases can provide lenders with the tools needed to perform deep data analysis into factors like cash flow and transaction data that is otherwise too cumbersome to examine. To illustrate this, the news source explained that AI-enabled credit decision-making tools make it easier to provide loans to micro-businesses with volatile cash flow situations. AI systems can perform the deep analysis into their cash flow records to better determine if they can handle a loan.

      In consumer lending settings, such as auto finance, AI systems can use bank transaction data to analyse potential risk factors in those who lack a sufficient credit history to score in a traditional way. This makes it possible to expand the reach of your lending products.

      5. Digitised Loan Application Processes.

      AI assists with and even automates key tasks, including:

      • ID verification
      • Credit scoring
      • Product matching

      As consumers seek a faster, more responsive lending experience, there is an opportunity to digitise loan applications to create stronger experiences. In many ways, this is the culmination of the other ways AI expedites credit assessment processes: Smarter, faster credit decisions allow for stronger digital lending capabilities. This is evident in how auto dealers are using AI to analyse market conditions to provide product recommendations to customers based on the price-to-value ratio of a vehicle listing. From there, AI systems performing credit assessments can give buyers insights into the actual loan products that are best for them based on their credit profiles.

      Beyond Assessments: AI Transforming Auto Financing

      AI has the potential to transform many of the processes surrounding credit decisions. The capabilities that make this possible can promote stronger customer experiences. It does this by:

      • giving businesses and lenders the ability to offer more personalised experiences. This can promote customer retention and better buying experiences;
      • providing end-to-end digital experiences. From using facial recognition solutions like Face++ to authenticate a user's identity to allowing for rapid loan decision-making as AI informs the lending process; and
      • enabling conversational commerce through chatbots and similar tools that provide deeper engagement when customers browse vehicles online.

      AI is creating new opportunities for digitisation and modernisation in auto financing and sales processes. Modernising lending operations is becoming critical in today's marketplace. AI technologies are creating opportunities to not only accelerate and digitise lending, but also create new value opportunities. Moving forward, AI still has years of evolution ahead. While the technology is ready to be used to address highly specific pain points, aggregating data from third-party databases, intelligently analysing large data sets and recommending decisions based on existing data parameters, end-to-end automation is still on the distant horizon. Automotive lenders looking to seize the opportunity of AI have a great opportunity ahead of them, but like many technology projects, the path to long-term success is often built around using the emerging technology to solve specific pain points while working through a long-term roadmap for broad adoption.



      Written By:
      Murad Baig, Chief Innovation Officer and Chief Product Officer OTOZ, NETSOL Technologies, Inc.





      Back to Articles

Downloads

View our collection of material and relevant resources pertaining to the global finance and leasing industry. Download Infographics, Profiles, Videos and Whitepapers.

Explore