Risk management is a crucial part of any financial institution, risk management plays an important role in revenue generation and a good risk assessment can assure new investors of how healthy an institution is with over 1.4 billion credit invisible consumers in the world, today and a lack of sufficient traditional credit data, the establishment of AI and deep learning innovations, fintech startups and financial institutions can help build that platform where risks can be managed using alternative data instead of traditional methods.
According to research by FT Partners, the lending and credit industry has been the backbone of fintech for the past 10 years. High-performance computing (HPC) assisted by AI can now help banks understand and manage financial risk in real-time. Fintech can process large amounts of data quickly using HPC, as well as assessing risk factors. This is crucial to financial institutions as in the event of bank failure, they can quickly assess the potential risk to their balance sheets and customers.
Meanwhile, traditional risk relies on behavioral variables such as historic, late o,r missed payment to make an assessment. The assessments are obtained from credit bureaus but fail to look at thin credit files, individuals with no credit history, or those without access to banking services. While modern AI-powered techniques require both the historic data behavior and liability to pay with real-time data before an effective measurement of the real credit risk of a consumer can be provided, different from their payment history.
Sam Edge, Global Head of Fintech for Startups at AWS described it saying “Credit in the past was lent to people that had credit in the past. Anyone that doesn’t have previous credit or anyone that’s not part of the financial system wasn’t getting access to credit for those reasons,”.
Upstart, an AI-based personal lending marketplace makes use of non-traditional variables such as education, employment, and social media data to determine customer creditworthiness. It leverages the AWS AL/ML platform which is powered by NVIDIA’s accelerated computing platform to power its deep learning models for underwriting. This helps to reduce model training time by 40%, using a deep learning model, Upstart can approve 27% more borrowers at a 16% lower average APR.
AI In The Car Industry.
Telematic is one of the rising innovations which is powered by AI and is helping to shape the auto insurance industry. Telematics is a mechanism that sources data based on individual driving habits. Telematics data are obtained from devices that are installed in cars to monitor driving habits and make use of GPS to safely assess how the person drives. Autotech companies like Insurtech and Root Insurance have also built telematics solutions for mobile devices, which makes it much easier to implement advanced technology in any car.
Also, personalized risk management can lead to a healthier balance sheet for both insurers and customers. Customers benefit by getting lower rates when they know their risk is being priced based on their driving behavior, instead of being based on outdated demographic data and credit scores. While insurers, on the other hand, benefit by having access to a more accurate understanding of the risk associated with each driver, based on certain factors such as acceleration, braking, cornering, and time of day driving. This relationship provides better protection for both the insurers and customers.
Edge shares that “If you are pricing somebody based on their driving behavior, they’re actually more likely to drive more safely,”
“You’re incentivizing good behavior because the driver knows that if they’re speeding or if they turn too sharply, that’s going to impact their policy and how much they end up paying for insurance.”
Alternative Data versus Credit Reports.
Open banking has become the leading charge in the new trend of lending innovation, since it allows companies to access previously unavailable data. This has helped many lending platforms to seek new ways to streamline the traditional lending process using AI-enabled models which analyze alternative data sources and can guarantee borrowers’ credit risk, even if there is no historical data. Alternative data also supports fintech in catering to the unbanked segments of the market, going as far as quantifying the risk of people who are unbanked.
Therefore, non-traditional elements can now complement traditional credit models. Regardless of whether the customer uses a Gmail or Hotmail account, their phone charging habits, or their online activity patterns during certain parts of the day, can all contribute as factors to influencing credit models for companies even in the lack of traditional banking data.
Lendio, a loan marketplace uses NVIDIA and AWS platforms, which enables access to capital for underserved communities and small business owners by matching them with various networks of lenders. It makes use of machine learning and AI to assess risk and optimize pricing, and through the accelerated risk-scoring process, small businesses can now have access to capital as quickly as possible.
Abby Sleight, Data Scientist at Lendio adds that “We firmly believe that lending automation expands approvals and reduces discrimination and bias,”
“This is supported by a National Bureau of Economic Research paper that found, when looking at data from Lendio and others from the CARES Act’s Paycheck Protection Program (PPP), the more automated the lender was, the less likely minority business owners were to be discriminated against.”
AI is becoming essential for success across financial services due to how much banks, in the realm of intellectual property, insurance companies, and asset managers differ. AI can now help fintech improve on their data insights which help in making informed decisions and identifying opportunities that others may miss.
Kevin Levitt, Head of Global Industry Business Development for the Financial Services at Nvidia adds that “In fintech, it’s not about how fast you can build a widget and get it onto store shelves,”
“It’s about how many different variables and insights can you leverage in making a decision around acquiring a customer, servicing a customer, and helping a customer along their financial journey.”
Powering ESG Goals with AI Solutions
Financial inclusion, a core piece in the ESG mission and for fintech, makes use of AI-powered solutions to expand the branches of the financial system to people who are underbanked or unbanked. AI-powered underwriting helps customers with the ability to obtain credit cards and build their credit even without a credit history or annual fees.
While investors also can collect and analyze more information than before, taking into account environmental, social, and governance risks and opportunities. This is soon becoming a norm for financial institutions to incorporate ESG-related risk into capital reserve calculations which makes them all the more crucial to understanding ESG factors better and accurately, be it satellite imagery monitoring or deforestation rates or using natural language processing to analyze company reports for mentions of ESG-related issues.
Clarity AI, a sustainability tech platform, makes use of Nvidia and AWS platforms to deliver environmental and social insights to investors, consumers, companies, and governments. Clarity AI integrates environmental awareness into online shopping, showing whether a company has lower greenhouse gas emissions than others or if a company is transparent in its reporting of climate-related information or not.
Ron Potok, Head of Data Science at Clarity AI says “Our partnership with AWS and NVIDIA has allowed us to perform hundreds of thousands of large-language model inferences per day, providing efficient and accurate predictions for our ESG platform,”
Aella, a West African instant loans and microlending company, makes use of computer vision technology which is powered by Nvidia and AWS to instantly verify a loan on whether the applicant is providing reliable evidence of income source. The applicant is required to take a selfie and a picture of a document that authenticates their employment and income status, which is matched using Amazon Rekognition, which verifies their identity and immediately releases funding.
