How Is Automated Credit Decisioning Transforming Digital Lending
Banking | 5 min READ
    
Evolution Of Credit Decisioning In Lending
The method of measuring risk with credit scores emerged somewhere in the 1950s. A research paper published in 1997 also foreshadows the likelihood of lenders being absent in the traditional brick-and-mortar business in the future - and links it back to the concept of credit scoring and the consequent function of the lender. Fast forward to the 2010s, and papers were already being published on the applicability of Machine Learning models to the practice of predicting defaulters and risk benchmarking for doing business in the financial services industry. Today, some of these prototypes are responsible for approving or disapproving a loan disbursal at some of the largest finance companies in the world.
Preeti Agarwal

Global Program Director

BFSI

Birlasoft

 
What Is Credit Decisioning In Lending?
Credit companies deal with risk daily, and credit decisioning is the gatekeeper that is supposed to keep the bad risk out of the business equation. Traditionally, credit companies have deployed complex statistical models that account for multiple variables constituting a credit applicant's profile to assign an internal score, which links the degree of risk that a loan represents in the real world to the company's business rules. Done manually, credit decisioning comes at long and expensive person-hours and falls short of the business needs of operating in today's economic environment by accumulating risk opaquely and eroding control in the enterprise. The Bank for International Settlements now advocates for information and analytical systems that can help bankers and lenders to monitor and assess credit risk in addition to a reasonably granular composition of the credit portfolio.
Challenges With The Traditional Ways Of Credit Decisioning
Traditional lending journeys take the user across approximately 50 different screens and take the user to the approval or disapproval state in several days. Despite being front-office digital, such lending journeys are fraught with manual processes that leverage legacy software and fragmented approaches to data aggregation at the point of credit decisioning. Being human-intensive, such models cost companies a fortune in the process of scaling up and often fail to capture applicants without a verifiable credit history. Beyond this, long waiting periods also result in higher dropout rates, low satisfaction, and reduced business value for the stakeholders.
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How Is It Hurting The Fortunes Of Lenders?
Traditional means of loan decisioning can stunt the growth of a lending company in more than one ways:
  • A misfit in the digital world- Manually-driven credit decisioning processes do not fit in today's digital context. Customers expect money instantly and physical goods on the same day or the next.
  • Uncaptured, untapped value- Relying on systematic financial and credit histories, traditional loan decisioning frameworks exclude several potential points of sale and user personas from the company's radar.
  • A digital illusion- Lurking behind seamless frontends, traditional lending not only comes at a higher cost but also bottlenecks the rate of growth and the possibility of upscaling.
  • Lack of visibility- In addition to being error-prone and subject to bias, manual decisioning also brings opacity to the enterprise's overall risk exposure.
Top challenges with traditional credit decisioning
Top challenges with traditional credit decisioning
Lending Digital Transformation: Powered By AI And Automation
How it works
Artificial Intelligence and automation have explored the frontiers of profitability, efficiency, costs, and end-user experience in the lending landscape. With customers' footprints now distributed around the enterprise's digital presence and various social networks, credit decisioning is taking a new turn. Leading lenders leverage unstructured and structured data from these footprints in conjunction with third-party data sources to train rule inferring models with machine learning credit scoring techniques like support vector machines, random forests and ensemble learning, and hybrid genetic algorithms for automatically tuning the model and refining feature selection strategies. Trained for over 99% accuracy, such models can eliminate bias and error and increase churn volume while reducing the need for human touch across most cases.
In conjunction with AI, automation can bring speed to the application-to-disbursal process and help tame the low-complexity application cases at minimal resources. However, some of the best automation implementations automate 70-80% of the cases - moreover, automation becomes challenging in high-complexity commercial lending portfolios. Relying on front-facing portals for automating the collection, warehousing, processing, and decisioning, end-to-end automation can help create new value propositions like instant processing and pre-approved loan offerings.
Benefits Of Using AI And Automaton In Lending
For Lenders
Cost benefits
AI-based credit scoring can reduce the cost of origination of loans by up to 40% and reduce the cost of decisioning on low-complexity cases. Also, lenders can scale their operations to new geographies and higher volumes with a marginal increase in human workforce capacity.
Time benefits
AI-based decisioning systems account for thousands of customer characteristics and automatically reinforce rules that keep the system under granular control and precision. As a result, these systems can cut hours and days' worth of human hours into seconds, and the overall lending journey to around 4 minutes.
Strategic benefits
In addition to reducing maintenance costs, achieving better scalability, and enhancing compliance, AI-based decisioning allows lenders to bring first-time borrowers and those without a credit history into their target groups. Moreover, machine learning can help enterprises achieve better segmentation and consequently one-up their risk management capabilities.
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For Customers
24x7, anytime, anywhere
With AI-powered automated decisioning, lenders can enable 24x7 operations and eliminate the need to shut down or confine operations by physical presence. Since these systems rely on cloud infrastructure, they also come with dynamic scalability, thereby ensuring consistent and high user satisfaction, even during peak times.
Seamless borrowing
With end-to-end automation of the application to disbursal process, customers never upload the same documents twice - what's more, the process's outcome can often be predetermined, thereby allowing the lender to conditionally approve loan offers before the completion of an application.
Inclusivity
Customers who are a part of the gig economy and first-time borrowers with little to no credit history will now be able to avail of loans based on healthy financial behaviors captured in their aggregating habits over time. Moreover, as the learning process is fine-tuned to eliminate bias, lending and business rules will become contextually sensible to buyers.
How are digital technologies transforming lending?
How are digital technologies transforming lending?
Beyond credit decisioning, AI is rewriting the financial value chain from scratch. Tagged as a trillion-dollar opportunity over the next five years by BCG, capturing growth in this fast-moving industry calls for a simple, next-gen tech platform backed by smart back-office processes. While decoding customer experience will be the key to finding volume, freeing up funds and reducing the average cost per decision can help disruptors lead and compete on the value-add model. In the future, critical partnerships with Fintech startups will be crucial for success in the larger ecosystem. To sum it up, lenders should start acting like tech giants and become proactive champions of their customers' voices before the tech giants do.
 
 
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