The Phenomenon of AI in the Equipment Leasing IndustryBy Dr. Ali Ahmed, Chief Data Scientist, NETSOL Technologies on 06-02-2022
The phenomenon of Artificial Intelligence (AI), to many, is still an emerging technology that sounds futuristic. However, the future is now, and AI has manifested its way across various industries. In simplistic terms, AI refers to the development of technology able to execute tasks and functions which require human intelligence, including speech recognition, visual perception and most significantly, making decisions. According to a PricewaterhouseCoopers (PwC) report, AI is forecasted to contribute up to US$16 trillion to the global economy by 2030. The report identifies financial services, automotive and healthcare as the three sectors with the greatest potential for product enhancement and disruption due to AI. Additionally, McKinsey Global Institute estimates that by 2030, 47% of the workforce in the US will be automated.
AI can greatly improve operational efficiency for equipment finance and leasing companies and other financial institutions, by empowering these enterprises to attain deeper insights, make better and more informed decisions and gain a significant competitive advantage. In a report by Accenture 'Tech Vision for Equipment Finance', it was stated that a staggering 93% of equipment finance professionals said that their companies were piloting or adopting AI.
As with other industries, AI enables the automation of repetitive and mundane tasks and with automation comes operational efficiency. Operational efficiency, in turn, substantially reduces costs and increases revenue and profitability for organisations. Processing documents takes a matter of seconds and automated information gathering with less manual intervention decreases the risk of error. The auto-decisioning process is, hence, greatly enhanced for equipment finance and leasing organisations.
Moving forward, for equipment finance and leasing companies, AI-enabled systems will decrease operational costs for them as a greater number of applications can be processed more swiftly and effectively. This can, in turn, lead to lower-priced loan products, due to reduced application fees for customers that procure heavy equipment.One of the core difficulties faced by financial institutions globally is delinquency and the risks associated with it. Failure to pay loans or leases on time creates a number of serious financial hardships on even the largest financial organisations, whether banks, equipment or auto finance and leasing companies. Especially during these unprecedented times, with the financial difficulties imposed by the pandemic, lenders worldwide are in a precarious position whereby traditional underwriting principles or guidelines to classify borrowers do not suffice. It is not sufficient to anticipate and differentiate borrowers who will pay their loans or leases in a timely manner with those that do not.
Delinquency risks are at the forefront of all crises financial institutions face and have the greatest outcome on their revenue and profitability. Delinquency risk management is therefore a fundamental constituent of a financial institution's risk management practices. Methodologies used for assessing risk pertaining to loans and leases have essentially become model-driven, whereby organisations use various credit scoring and rating models to anticipate loan delinquency, whether for loans, leases, financing, mortgages or credit cards. AI-enabled algorithms analyse data and history in-depth and detect potential risks. They analyse historical data pertaining to loans alongside real-time activities, and subsequently, provide precise predictions and projections based on various variables. Fraud detection becomes extremely precise via AI, whereby if an AIpowered system detects a potential fraud, but a human being ascertains that it is not a fraud, the AI-powered system incorporates that knowledge via humans and will not classify that as fraud in the future.
When integrated with equipment finance and leasing systems, AI can help determine fraud via the analysis of both past and present consumer behaviour, consumer location, purchase preferences, alongside a number of other factors. In terms of credit underwriting, AI therefore provides a precise assessment of a potential borrower via a rapid and accurate process. With lowered costs and an overall view of an applicant, decisions pertaining to the customer are taken backed by data which are better informed.
Equipment finance and leasing systems that are integrated with AI for credit underwriting are therefore not biased and are more reliable when compared to conventional credit underwriting systems, thereby encouraging and demonstrating fairer lending practices. It is also essential for every financial institution to protect people from discrimination based on various personal information. Machine learning (ML) is a type of artificial intelligence (AI) that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so. Machine learning algorithms use historical data as input to predict new output values.
For the equipment finance and leasing industry, ML models use algorithms and models in order to make accurate predications to calculate credit worthiness and predict loan repayment. Using various data variables and with a provided set of data consisting of vital data points including the term of the lease/financing, the amount that is financed, monthly installments/payments plus other data, ML models can predict which blend of variables can influence loan performance. AI/ML enable process automation in order to streamline and automate a number of functions, including entry of data, reviewing applications, generating invoices, credit underwriting and scoring and customer self-service. Further, one of the prime use cases for AI is in providing smart recommendations to customers. This is particularly useful when the number of offered financial products is very large.
When further analysing credit decisioning and risk assessments, AI credit workflows have several advantages over conventional credit workflows. It is a manual burden to revisit/add more conditions in decision-making workflows and more sophisticated workflows don't scale well. Further, as stated earlier, manual intervention of credit analysts is required to evaluate applications. AI credit workflows, on the other hand, can assess the relationship between data and creditworthiness. These models can be trained on millions of consumer data items (age, job, marital status, etc.) and financial lending or insurance results (has the person defaulted, paid back the loan on time, etc.).
Model decision reports can provide insights to customers about parameters influenced in given decisions. No manual intervention is required and an instant response can be provided on POS. AI-based credit risk models check the 360-degree view of every application, taking into account multiple factors, including but not limited to, customer financials, behavioral facts, equipment details, transactional data and credit information. Further, with continuous monitoring and learning, models will be up-todate with application data.
A POS instant decision response enables dealers to instantly recommend more suitable choices to customers. RISKROBOT by Spin Analytics provides different credit risk models in software for automation, while Enova the financial company, claims to incorporate 90% AI models in their credit risk pipelines. AI powered Robo Advisors and Chatbots provide a seamless and superior experience for customers and assist in dealing with queries and resolving issues without the need of human beings, thereby greatly reducing human capital expenses, particularly in terms of call center costs. With instant responses, Robo Advisors provide an unparalleled experience. Robo Advisors recommend products to customers based on their profiles and credit risk assessments.
With AI-powered credit risk decisions, Robo Advisors are enabled to provide more economical product recommendations and options to customers. The Bank of America launched its AI-enabled Chatbot Erica29 and it is available through voice or message chat on the bank's mobile app. In 2016, Ziyitong was established and launched an AI platform to help recover an estimated RMB150bn in delinquent loans. The AI platform helps recover delinquent loans for approximately 600 debt collection agencies and over 200 lenders (including the Postal Savings Bank of China and Alibaba).
For portfolio management, AI models can reduce risk in equipment/asset management by increasing the return. AI-powered models integration in broader and deeper data (structured and unstructured data) can generate insights for these decisions. Amelia is an AI-powered Chatbot that assists Allstate Insurance employees. Originally deployed in September 2017, Amelia has helped call center representatives with more than three million customer conversations. Integrated with AI (for the credit underwriting process), NETSOL's next-generation platform NFS Ascent is a highly adaptive solution which enables equipment finance and leasing companies to future proof their business. It is also available via deployment on the cloud.
If you would like more information about the product, you can contact us by clicking here. Further, if you would like a FREE demo of Ascent on the Cloud for your finance and leasing business, please click here.
- https://www.pwc.com/gx/en/issues/data-and-analytics/publications/ artificial-intelligence-study.html
Dr. Ali Ahmed, Chief Data Scientist, NETSOL Technologies
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