In today’s world, events across the globe have a faster and deeper impact on lending.
The rapid changes in the global economy, the consumer’s adoption of new technology, and innovations in credit access and lending products are changing the way lenders are competing for business.
Financial institutions have been pushed to adopt digital lending, to find new ways to compete for loan growth, to build resiliency, while complying with more regulatory requirements.
77% of Financial Institutions had reduced returns in the past years, and in the meantime, 53% of the US market is underserved and would benefit from alternative credit.
Using automated AI systems to develop lending strategies has proven to provide lending success across all functions and products.
Ultimately, lenders access high rewards while increasing engagement with borrowers: reducing workload from months to minutes, increasing revenues by 40%+, reducing lending analytics and compliance monitoring costs by 95%.
Below are 10 insider tips on how to use AI to develop your roadmap to lending success.
1. Improve your internal strategy process to adapt to faster changes
- Why having an adaptive lending strategy model is important?
The global financial crisis, changes to regulatory frameworks, the uncertainty in economic recovery from the recent global pandemic, the increase in inflation, and the tightening of financial conditions have had a marked impact on the banking sector globally.
Internally, lenders must translate these economic changes into growth opportunities or credit loss mitigations initiatives. These efforts may take several months to develop, validate, and implement across multiple functions: credit, fraud, pricing, product, operations, collections.
At the end of the day, the disconnect between lending functions, the overlap work between each lending product vertical, the tech debt, the poor data quality, the limited availability of skilled analysts, and the complexity of internal processes are just a few examples of problems that may impact lenders’ business success.
- Automated AI to improve strategy development and validation process
The progress in automated Artificial Intelligence/Machine Learning for lending strategies, combined with lending expertise and forecasting tools across all functions and products, provides lenders a new alternative to reduce the lag between global events and lending changes.
These advanced techniques are transforming lending strategy development with the goal to improve data interpretation. With the advance in computer automation, it is also critical to maintain human control with the possibility to manually review, edit, test fair lending, and stress test supervisory and custom economic scenarios.
2. Leverage expertise to reduce costs and accelerate regulatory changes compliance
- The increased pace of regulatory changes
The 2008 financial crisis pointed to weaknesses in the financial system and has brought additional regulatory requirements.
Today, lenders must spend more on supporting functions and compliance reporting to keep up with regulatory changes: BSA amendments, Dodd-Frank, TILA-RESPA, QM rules, CARD Act ability to repay, Basel III CCAR/DFAST, CECL…
According to a recent Congressional Research, US banks are spending as much as $25.7 billion a year on regulatory compliance. Data has shown that the burden of compliance falls more heavily on smaller banks. For example, banks under $100 million dedicated 9.8% noninterest expense to compliance, while banks between $1 billion and $10 billion dedicated 5.3%.
- A solution to reduce costs and help with compliance controls
The Regulatory Technology (RegTech) industry has established a solid foundation within the FinTech ecosystem to overcome this and come up with solutions that are targeted to new and complex regulations, litigation, and regulatory remediation areas.
Industry compliance expertise combined with AI offer lenders the possibility to facilitate implementation, get independent assessments, and access regulatory compliance resources while reducing internal cost.
3. Don’t forget to enhance your core lending strategies when moving to digital
- The growth of digital banking
Nearly all banks and credit unions plan substantial investments in their digital offerings. By the end of 2022, 89% of US banks and 96% of credit unions will have launched a digital transformation strategy.
However, digital banking transformation is a long-term strategy with many short-term challenges. The transformation journey is taking most organizations at least twice as long and costing twice as much as originally anticipated. 53% of the organizations surveyed remain untested in the face of digital challenge and their digital transformation readiness is therefore uncertain.
- Using AI beyond digitization
For years, AI has been used in lending to streamline document capture and review processes and to speed up underwriting. AI is now moving to lending strategy development.
When combined with lending expertise, it provides advanced analytics, expert strategy development, robust validation with easy-to-understand explanatory rules and programs.
4. Leverage external industry expertise to acquire high demand knowledge
- Difficulty to attract high demand AI talent
2022 marks the jump for both banks and credit unions in the percentage of executives concerned about their ability to attract qualified talent. 65% listed this as a top concern for 2022, up from 19% in 2021.
The most difficult capability to acquire for Financial Institutions is Artificial Intelligence/Machine Learning at 49%. Finding talent with a combination of Artificial Intelligence / Machine learning expertise and lending experience gets even more difficult.
- AI systems remove hiring problems and lower cost
New AI systems provide 10 to 15 times the value of a single data scientist at a lower cost. This is especially convenient in geographic areas where AI talent is not available or where wages and cost of living are high.
5. Compete more effectively in the underserved lending market
- The large underserved US lending market
One of the obvious market opportunities for lenders is that 53% of the US market is underserved and would benefit from alternative credit.
Underserved US consumers may struggle with one, two, or all three of these financial challenges: 67 million consumers struggling with LMI or volatile incomes, 91 million credit-challenged consumers with a thin-to-no credit file or a subprime score, 63 million unbanked or underbanked consumers (FDIC designation).
- Recent industry insights and regulator feedback
The non-bank lenders have been leading the way by adopting the use of alternative data to assess creditworthiness: consumers’ deposit and spending patterns, utilities or rent payment information, court information.
In December 2020, the Consumer Financial Protection Bureau issued rules that may facilitate the use of alternative data.
For example, one rule changed the general qualified mortgage definition to give lenders additional flexibility—which could include analyzing alternative data such as cash flows—when assessing a consumer’s ability to repay.
A recent US interagency statement highlighted potential benefits and risks.
Expanding lending operations into the underserved market is a critical way in which lenders can not only increase their own revenue but also compete more effectively.
Risks may be addressed with appropriate advanced programs, testing, monitoring, and controls to ensure both lender protection and consumer financial health.
- How to serve this market at a lower risk
From the lender’s perspective, this assumes a significant initial investment in technology stack to collect alternative data sources, a robust data infrastructure for testing, and in-house alternative data expertise which is most likely difficult to quickly acquire for most small to mid-sized lenders.
AI systems pre-built with lending expertise and with the capability to test alternative data are the best option for small to mid-sized lenders to quickly and safely cater to this underserved segment.
6. Commit to Financial Health beyond credit training programs
- Financial health concerns
In January of 2020, 33% of Americans were struggling to make ends meet before the pandemic began and many of them lack personal finance knowledge.
It’s likely that many of these people suffered financially over the past 2 years. Only one-third of Americans are considered financially healthy.
For lenders, investing in customers’ financial health early can help them build and maintain a competitive advantage in a crowded market.
When customers strongly agree that their bank looks out for their financial well-being, 84% are fully engaged, while none are actively disengaged. And when banks take time to address customers’ financial well-being, they strongly increase customers’ confidence in their primary bank.
- Industry innovations and the next lending strategy evolution
With the development of open banking and the increasing access to deposit and cash flow information, leading lenders consider financial assets, cash flow, and debt to income to measure financial health, and don’t only rely on credit scores for underwriting.
Beyond customers’ financial health training, insights, and alerts, new approaches that will have a deeper impact on customers’ financial health program success are using modern digital and artificial intelligence capabilities.
For example, they are enabling day-to-day household financial management, facilitating financial debt accumulation and decumulation over time, and empowering customers to control their relationship with lenders.
7. Promote fair and more inclusive lending
- Some historical background
Traditional lending techniques have shown limitations in promoting financial inclusion, as many banks have been hesitant to lend outside of their comfort zone.
Most lenders have historically used traditional data and limited strategies for underwriting which benefited the same credit spectrum and strengthened the same types of customers’ credit profiles.
- Progress with advanced analytics and AI
Recently, lenders that have incorporated alternative data and AI models have demonstrated progress in closing the inclusion gap.
The predictive patterns identified by AI models can help lenders widen credit access. For example, by identifying previously overlooked but financially healthy customers. Successful inclusive lending programs have also required systemic and cultural organizational change.
- AI fair lending management best practices
As with all new solutions, new AI programs may come with potential risks. For example, bias due to the availability of the data for more digitally active or higher-income individuals, creating a potential for discrimination, or privacy concerns if consumers lack knowledge and control on how these data are used.
Several key measures must be used to address the potential risk of using AI for lending strategies and require advanced lending and technical expertise:
- Set clear and robust regulatory expectations regarding fair lending testing to ensure AI strategies are non-discriminatory and equitable.
- AI engines need to be fully compliant with regulatory requirements, such as consumer privacy, US Reg B, FCRA, and model risk management supervisory guidance.
- Input and output data need to be tested for any statistical bias prior to using AI. For example, using advanced explanatory data analysis. For unbanked segments, temporary pilot programs may be needed to collect additional information.
- Provide explainability in AI results. Lenders need to be able to understand, document, and validate the AI output on a regular basis. It also provides clear Adverse Actions for underwriting.
- Document monitoring programs for fair and inclusive lending through ongoing independent audits.
8. Diversify your lending products and services
- New segmented lending approach
To address the diversity of consumer profiles and needs, the lending industry is seeing innovation in the services that lenders provide and who they provide to.
Some Fintechs are focusing on specific segments such as postgraduate education, minority-owned businesses, energy-efficient, or community enhancement financing. Specialist lenders will play a critical role in sustainable and inclusive post-pandemic recovery.
In addition, banks are losing revenue in two major lending markets: the unsecured lending interchange fee revenue from the growth of non-bank payment companies (e.g. Apple Pay, Google Pay,…) partnering directly with merchant mobile apps, and the growth of online mortgage FinTech: nonbank mortgage lenders in the U.S. issued 68.1% of all mortgages originated in 2020, up from 58.9% in 2019.
The recent consumer interest in cryptocurrency has also contributed to the emergence of Decentralized Finance (DeFi) lenders. These have lending protocols where users can lend and borrow crypto assets.
Buy Now, Pay Later (BNPL) is a type of short-term financing that allows consumers to make purchases and pay for them at a future date, often interest-free. 39% of Americans say they’ve tried BNPL at least once. Also referred to as “point of sale installment loans,” BNPL arrangements are becoming an increasingly popular payment option, especially when shopping online.
- The industry constraints and the path to innovation
Most lenders won’t be able to compete directly in these new innovative segments mostly due to technology, lending expertise, and data access constraints.
Leveraging vendor offerings with instant access to AI strategy development is most likely the only way for FinTech startups, small and mid-sized banks, or credit unions to quickly learn, innovate, and diversify in lending products and services.
The maturity of alternative data from key vendors and the increasing availability of payment (e.g. open banking, partnership, or consortium) and credit data (e.g. BNPL trade data available at US Credit Bureaus) will only increase the knowledge gap between lenders and the lending competition edge.
9. Invest in technology and FinTech AI partnerships
- Current industry trends
As the FinTech segment has been growing at a rapid pace, banks and credit unions are investing in technology.
77% of Financial Institutions had reduced returns in the past years and 86% of US banks and credit unions will increase their technology spending in 2022 from 2021, with about 25% growing their tech budgets by more than 10%.
- Keep up with the competition through strategic FinTech AI partnerships
According to a recent survey, 69% of credit unions’ executives had “Improve efficiency”, and 61% of banks’ executives had “Improve customer experience/service delivery” as their top technology priority.
One shift worth noting is the 3X increase in the percentage of financial institutions listing FinTech partnerships as an important priority, with 23% of executives’ feedback for the credit unions and 15% for the banks.
New secured, automated, and independent FinTech AI solutions specific to lending strategies and supporting functions are emerging and are focusing on improving lenders’ competitiveness, efficiency, and product delivery. Early adopters will benefit from innovations applied at the heart of lending.
10. Roll out your successful AI programs across all functions and products
Following initial AI pilot programs, lenders should roll out new AI programs across all functions and products to maximize their lending success at the enterprise level.
There are multiple use cases for AI at the strategy level from the front-end account acquisition to the back-end account management. Having an AI system instantly accessible allows lenders to quickly develop, validate, and compete while offering fair, inclusive, and compliant lending products.
Some examples of use cases for AI lending strategies:
- Product: Product roadmap strategy for all products, channels, and product features
- Member/Customer: Membership, engagement, retention strategies with life cycle sensitivities
- Revenue: Strategies enabling market share expansion and service to underserved segments
- Marketing: Preapproved and cross-sell campaigns strategies, CPA, and content optimization
- Finance: Return On Asset, investment, capital requirement, and CECL IFRS 9 loan-loss reserve optimization
- Pricing: Risk-based pricing with key product characteristics to optimize revenues
- Credit Risk: Advanced Credit Policies and Underwriting strategies. Alternative lending. Stress testing
- Fraud: Behavioral analytics and fraud detection strategies for Digital Lending expansion
- Compliance: Regulatory compliant explainable models. On-demand model validation friendly documentation
- Account Management: Account management and servicing strategies. Work queues and operations analytics
- Collections: Early and late-stage collection strategies and process optimization
Conclusion
We’ve covered 10 Insider Tips For Lending Success Using AI.
We’ve highlighted recent key lending market trends, statistics, and connected them to AI; now you know what AI for lending strategies is, what it can accomplish, and the steps for reaching your own lending success.
Now that you know this, you will be able to achieve your goals while increasing engagement with borrowers. Some of the most recent industry lending strategies breakthroughs using AI: reduced workload from months to minutes, increased revenues by 40%+, reduced lending analytics, and compliance monitoring costs by 95%.
Want to learn more about AI for lending strategies? Request a demo of Prometics Lending NavigatorTM and subscribe below to get the latest industry insights, products updates, community events, and other news.