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SPOTLIGHT Sponsored by: Provenir

The business of lending money is changing — and rapidly. Innovative technology, fueled by expanding data and artificial intelligence, is redefining borrowing.

New Data Sources and Innovative AI Are Redefining the Business of Lending September 2022 Written by: Aaron Press, Research Director; and Raymond Pucci, Research Director

Market Overview The business of lending money is rapidly changing. Innovative technology, fueled by expanding data and artificial intelligence (AI), is redefining borrowing. Traditional lenders, as well as alternative sources such as nonbank and "buy now, pay later" (BNPL) providers, are reimagining how people and businesses obtain credit. Intuitive online interaction and rapid delivery are becoming increasingly common across a range of services. Consumers have grown accustomed to the ease and efficiency of ecommerce, which in turn has changed the way they think about the customer experience in a variety of other business interactions, both personal and professional. Lending decisions, meanwhile, have been made in much the same way for decades, leveraging labor-intensive, meticulously built risk scoring models based largely on traditional data. This limited data is then combined with relatively blunt rules and binary decision points that can needlessly leave borrowers without access to funds and lenders without access to customers. But the same advances that are changing experiences and expectations in ecommerce are finding traction in lending, making ecommerce levels of customer experience possible. Data about consumers, businesses, and transactions is growing in both volume and availability. AI and machine learning (ML) are creating opportunities to leverage that data for advanced analytics and risk modeling (see Figure 1). And cloud computing is allowing fintechs and banks to bring new functionality to market with increased speed and less infrastructure (see Figure 2).

AT A GLANCE

KEY TAKEAWAYS » Artificial intelligence (AI) technology, accompanied by new and diverse data sources, is critical to enabling accurate and time-sensitive digital lending approvals. Financial institutions (FIs) and fintechs are investing in AI-based risk decisioning technology that adds agility and process productivity to their decisioning systems. » Models using AI and powerful data ecosystems play a critical role in adding speed and scalability and ensuring accurate and equitable loan underwriting. » Lenders realize positive outcomes and revenue growth from expanded loan throughput, higher approval rates, reduced fraud, and fewer credit losses. » Borrowers, including unbanked and underbanked consumers, also realize benefits from AI-based risk decisioning such as enhanced customer experience, fast decision response time, more accurate reflection of financial health and ability to pay, personalization of current and future borrowing needs, and optimal pricing.

SPOTLIGHT

New Data Sources and Innovative AI Are Redefining the Business of Lending

FIGURE 1: AI Use in Loan Portfolio Optimization/Risk Assessment Q At what stage is your company in investing in AI?

(2.0%)

(9.0%)

In production in business units or departments

(31.0%)

Pilot/proof of concept

(13.0%)

In production enterprisewide

Researching/under consideration

Considered not yet pursuing

(18.0%)

Not considering

(27.0%)

n = 100 Base = respondents who indicated organization's principal business activity is banking Source: IDC's Industry AI Path Survey, May 2021

FIGURE 2: Cloud Use in Loan Origination Q Please indicate your organization's plans for cloud use in loan origination.

(1.7%) (1.7%)

(35.8%)

(3.5%)

Currently use cloud and purchased it more than 12 months ago

(6.9%)

Currently use cloud and purchased it within the past 12 months

Expect to move to cloud within 24 months

Have moved this from the cloud back to on premises

(20.8%)

Have always run this on premises and do not plan to move to cloud

Do not have this capability at all

Don't know

(29.5%)

n = 100 Base = banking respondents Source: IDC's Worldwide Industry CloudPath Survey, May 2020

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SPOTLIGHT

New Data Sources and Innovative AI Are Redefining the Business of Lending

Lenders that embrace AI and cloud technologies have clear advantages. Enhanced data and smarter data models can both reduce risk and increase approvals. Better pricing can attract more customers. Automating decisions reduces the cost of manual processing. Faster decision making means that customers are more likely to accept an offer. But these advances are not without their own challenges. Data availability has expanded greatly, leaving many stuck trying to figure out how to extract the value. Looking at the wrong types of data can impact the effectiveness of a model. Lenders need tools to access the right data at the right time. Advanced analytics, while more accessible than ever, require careful attention. For lenders, this set of realities makes choosing the right data and decisioning partners more important than ever. Better Data and Better Analytics Displace Traditional Lending Practices Modernization and digitization of core functions such as credit decisioning, fraud, process automation, and customer experience are driving financial institutions (FIs) and fintechs to invest in big data, AI, and cloud to meet growing borrower needs. Loan customers look for a friction-free and more personalized interaction with their lenders. In return, borrowers often come back for more financial services, which becomes a foundation for long-term customer relationships. Making this type of interaction a reality requires additional data. But more data is not the same as better data. And not all decisions will benefit from the same data sources. Each lending decision has its own set of risk parameters depending on the borrower and their goal for the funds. Using a one-size-fits-all data modeling scheme will limit the ability of lenders to optimize decisions and limit the population of consumers they can serve. This makes it critical that the right data, from the right sources, is brought into the decisioning process. It is important that decisioning be powered with the right machine learning models. With these models embedded as part of decisioning, drift is easier to monitor and can be fixed without impacting production. The combination of different data types and machine learning means lenders can make more accurate credit decisions, creating favorable outcomes for both lender and borrower alike, in personal and commercial lending. AI-powered models not only add speed and scalability to lending but also ensure an optimal customer experience. FIs and fintechs will find more revenue opportunities leading to sustainable customer engagement as they invest in AI-based technology solutions in their lending operations. And the key to maximizing the value of AI is robust data. Real-time data that addresses identity, fraud, and credit is critical to instant decisions that minimize risk because the models continuously learn. According to IDC's Worldwide 3rd Platform Spending Guide for banking (November 2021), tech spending on AI systems is expected to increase from $11.7 billion in 2021 to $27.7 billion in 2025, which represents a four-year compound annual growth rate (CAGR) of 24.2%. Clearly, FIs understand the significance of AI technology in credit decisioning and are on an aggressive growth path to install this technology. Decisioning is a core area for lenders to step up their digitization game to capture more market share and optimize revenue. Risk decisioning models that utilize a robust data ecosystem to fuel AI technology play a critical role in successful loan decisions.

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SPOTLIGHT

New Data Sources and Innovative AI Are Redefining the Business of Lending

Benefits to Lenders Using AI-Based, Data Fueled Risk Decisioning Models Lenders will find move-the-needle results when utilizing AI technology and multiple data types in their loan operations, including: » Optimized lending decisions. Traditional credit scores bypass unbanked and underbanked borrowers with limited credit histories and can often misrepresent those with established files. The combination of new data sources and AI can greatly expand risk assessment using hundreds of alternative credit data types, which leads to higher approval rates and better pricing. » Expanded loan productivity and growth. Loan origination and processing are highly labor intensive. An automated decision platform that includes AI and access to multiple data sources leads to higher loan application throughput with more accuracy. » More revenue/fewer credit defaults and losses. Fraud on applications includes schemes such as synthetic identity and account takeover. AI and ML algorithms detect fraudulent applications and identify legitimate borrowers who might otherwise be rejected if initially thought to be fraudulent (a false positive). » Ability to achieve scale and agility. With the right technology, FIs can quickly respond and successfully compete in the digitally native market. Further, no-code technology enables innovation and allows for changes in lending rules quickly. FIs can better manage loan growth over time and respond quickly to changes in loan application volume.

Benefits to Borrowers of AI-Based Decisioning Models Borrowers see benefits when lenders use AI for risk decisioning that results in:

» Enhanced customer experience. Consumers are accustomed to convenience and immediacy in any type of digital transaction. Loan processing using AI means more automation of manual tasks and a more streamlined end-to-end application process. » Fast decision response time. Delays in credit decisioning often lead to borrowers switching to another lender. AI models will recognize creditworthy borrowers with high accuracy, resulting in fast credit decisions and higher approval rates. » More accurate reflection of borrower credit history and current status. Many lenders have loan origination functions that perpetuate financial exclusion of borrowers without credit bureau scores. AI technology solutions are programmed to assess borrower risk from alternative credit data such as telco data, social presence scoring, device data scoring, utility bill payments, rent payments, and other financial transactions. » Personalization of current and future borrowing needs. Loan applications contain a wide range of data about a borrower's financial history and spending patterns. Using predictive analytics models, lenders can understand a borrower's current and future banking needs, which leads to cross-selling of other products and services. » Optimal pricing. Another type of personalization relates to loan fees and terms. AI models can identify borrower characteristics and anticipate future payment patterns, thereby setting optimal pricing and terms for the life of the loan.

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SPOTLIGHT

New Data Sources and Innovative AI Are Redefining the Business of Lending

Considering Provenir's AI-Powered, Data-Fueled Decisioning Platform Fintech and FI lenders operate in a highly competitive market, compelling them to deliver added value to their clients, including consumers, businesses, and merchants. Provenir provides these lenders with its AI-powered, data-fueled risk decisioning platform that runs as a cloud-native platform as a service (PaaS). This decisioning platform provides lenders with the flexibility and scalability to drive a credit risk strategy across various verticals and product offerings. Provenir's platform serves many loan types and vertical markets, including:

» BNPL » Fintech » Telco » Small business lending

» Retail and point of sale (POS) » Digital merchant onboarding » Auto financing » Banking and loan origination Provenir's technology is designed for rapid data integrations, fully automated decisioning, and real-time business insights through a no-code, drag-and-drop user interface (UI) that eliminates hard coding.

The company's decisioning platform provides the following benefits to client lenders:

» Customer base expansion. Lenders can approve more applicants than before by using AI and alternative data sources. The use of AI also results in more accurate assessment of risk factors. Financial inclusion draws additional customers as credit decisions will be based on a much wider array of alternative data. » Fraud identification. Data solutions aligned with risk decisioning deliver faster, more accurate decisions and fewer manual reviews to identify and prevent fraud during onboarding. This system maximizes approval rates by more accurately detecting true fraudsters and reducing false positives that are legitimate applicants. » Loan pricing refinement. AI distinguishes among various customer profiles and personalizes the right pricing for each applicant. This maximizes profitability, optimizing revenue and reducing future loan losses. » Customer relationship sustainability. The platform analyzes more customer data at a faster pace, enabling lenders to better match loan offers to applicants. Lenders can establish long-term customer relationships when proactively responding to future borrower needs. » Customer management optimization. Faster and smarter processing of real-time borrower data enables predictive analytics on customer accounts. AI technology detects patterns and signals from customer activity that can reveal risk factors leading to loan delinquencies. With these early warning signs, lenders can collaborate with borrowers to head off collection and recovery actions.

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SPOTLIGHT

New Data Sources and Innovative AI Are Redefining the Business of Lending

Challenges Provenir faces the following challenges in the risk decisioning technology space targeting fintechs and FIs:

» Increasing market saturation. While AI technology spending by FIs and fintechs is expected to grow significantly in the next three to four years, many technology developers and sellers are competing in this market. Risk decisioning vendors must demonstrate key differentiating features to technology buyers to set themselves apart from competitors. » High system cost perception. AI implementation costs can run high and can vary widely among technology buyers that have different IT systems and lending needs. Additionally, model integration may be dependent on the internal knowledge and existing data infrastructure of fintechs and FIs. Vendors will have to demonstrate that they can deliver on promises of rapid integration and strong return on investment (ROI). » Systems integration and alignment. Lenders look for ease of deployment of any new system. Technology providers must be able to show friction-free deployment and expeditious ROI. » Unfavorable perceptions. Some borrowers and lending stakeholders are concerned that AI technology lacks trustworthiness. Models may introduce or reinforce unintended biases, especially among underserved or underbanked borrowers. Others think that AI systems lack transparency, posing regulatory challenges for FIs. Risk decisioning technology must incorporate clear model governance and demonstrate a level playing field for all loan applicants. Conclusion Decisioning digitization drives successful lending at FIs and fintechs that serve consumer, business, and merchant borrowers. Among advanced technologies in the financial services sector, AI-based risk decisioning models fueled with nontraditional and alternative data stand out as game-changing systems that alter the competitive lending landscape. With the competitive edge provided by AI, lending decisions are more accurate, produce more revenue, reduce loan losses, and address financial exclusion. AI technology platforms are highly scalable and agile to meet the needs of both lenders and borrowers while producing a valued customer experience. IDC believes that risk decisioning systems are a critical path in a lending process that requires speed and a frictionless customer journey. Provenir has built a robust AI-based risk decisioning system accompanied by rich data solutions and is well positioned to meet the needs of FI and fintech lenders as they increase their technology spending.

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SPOTLIGHT

New Data Sources and Innovative AI Are Redefining the Business of Lending

About the Analysts

Aaron Press, Research Director, Worldwide Payment Strategies Aaron Press is Research Director for IDC Insights responsible for the Worldwide Payments practice. Mr. Press' core research coverage includes bank, corporate, and merchant challenges around the evolution of payment networks, systems, and technology; fraud and security risks; and legal and regulatory issues. Raymond Pucci, Research Director, Worldwide Consumer and Small Business Lending Digital Transformation Strategies Raymond Pucci is Research Director for the Worldwide Digital Lending Transformation Strategies practice. Raymond's primary research focuses on emerging solutions and use cases around lending transformation in financial institutions and fintechs that are targeted to consumers and small and medium-sized businesses.

MESSAGE FROM THE SPONSOR

More About Provenir Provenir helps fintechs and financial services providers make smarter decisions faster with our AI-powered and data- fueled risk decisioning platform With the unique combination of identity, fraud and credit solutions, simplified AI and world-class decisioning technology, Provenir provides a cohesive risk ecosystem to enable smarter decisions across the entire customer lifecycle – offering diverse data for deeper insights, auto-optimized decisions, and a continuous feedback loop for constant improvement. Fully configurable and scalable to support your business goals, Provenir's platform powers decisioning innovation across organizations, driving improvements in the customer experience, access to financial services, business agility, and more. Provenir works with disruptive financial services organizations in more than 50 countries and processes more than 3 billion transactions annually. Experience for yourself how a data-fueled, AI-driven solution can rapidly drive new innovation and propel your growth. To learn more about Provenir and our industry leading platform, visit our website: www.provenir.com.

The content in this paper was adapted from existing IDC research published on www.idc.com .

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