Different dynamics and different approaches evolving : Lending Technology Think Tank Summary

Like spring, there was a burst of energy in the Lending Technology Think Tank this week. Lots of new ideas and concepts coming through…

Clearly Q1 has been super busy for most folks, and as we know plenty has been happening economically, with regulation and in society, both in the UK and around the world… so there was plenty to discuss and think about for the future.

As always there were a couple of big ideas that really caught my attention and made me sit back and think.

  • There are very different responses in demand and arrears levels by market segment. Mortgages are a very flat, whereas unsercured lending is seeing increasing loan demand volume with affordability issues spiking
  • A discussion around affordablity. Can affordability prediction be used to measure whether products are helping customers tend towards better outcomes? (ie evidencing consumer duty)
  • Consumer duty is coming soon, but implementation readiness is mixed. This could be an issue with deadlines in April and July, and resulting in a very busy 6 months for some
  • Affordability is as much an issue about income rather than expense. Higher earners just have more wiggle room to not have to think about budgeting as much. They may be poorer budgeters as a result. This may cause issues as financial stress creeps up to higher income cohorts than before
  • We now need to think about not just the consumer -company relationship, but the consumer-company-regulator relationship. Regulators are taking a more active role. Will pricing be next (as the case in Ireland and in some other markets in the Eurozone)

It was a privilage to be able to chair again. Thanks to everyone for their expert insights, it is always interesting, and of course a thank you Colin White and the Credit Connect team for the invite and their organisation.

I have added my notes below… if you want to see the entire videos and all the discussion (including our discussion of sci-fi distopia at one point!) they are available on playback, register at this link

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Session Summaries

Session 1: Credit Risk and the Cost of Living Challenge

  • Customers struggling more than ever before; financially stable customers also facing hardship for the first time; rise in illegal money lending indicates customers are finding finance in different ways; challenge is how to reach those customers and have the right conversations
  • Responsible lenders are reevaluating policies during the current financial squeeze
  • Tips for budget planning: be blunt with people who say they can’t afford it, reconsider expenses like cars, keep an eye on interest rates for loans and mortgages
  • The future looks unpredictable and interest rates are going to be quite unstable for some time. Lending and credit risk models should be proactive in factoring that in
  • The increasing interest rates make it hard for consumers to predict their future outcomes and may restrict borrowing, causing concern
  • Providing more visibility around credit scores to consumers and finding value for the members
  • Firms are making use of customer data to better understand their needs and challenges, and adapting their products and services accordingly
  • Open banking is valuable because it provides real-time data, but it’s not the only source of valuable information. Traditional Bureau data can be just as predictive, and overlaying the two can lead to better decisions. Recalibrating models and updating data is crucial in a changing environment
  • Recency of data is important for loan decisions as borrowers’ financial situations can change between the time their data is received and when the loan is approved
  • Being transparent with customers and helping them be autonomous is vital for putting the customer in a position where they can take forward their financial needs right, and using technology that’s very data-driven, help the customer understand what went into that decision-making and how it’s been evaluated to put them in a position of empowerment
  • Achieving inclusivity in design stage using large language models to summarize customer feedback and core data
  • Companies are focused on putting customers first and ensuring they can afford it without getting into trouble, and see this as an opportunity to examine every part of the business
  • Lenders were more open to helping people during the pandemic.
  • Mortgage lending is at its lowest in seven years, and first-time buyers are starting to look and buy
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Session 2: Measuring affordability

  • Affordability differs depending on the type of lending being done. Mortgages require a more granular assessment compared to buy-now-pay-later retail loans
  • New forms of data, combined with analytics methods, are helping assess individuals with unconventional credit backgrounds
  • Real-time data and very quick decisions with open banking data. It helps assess customer expenses better and enables different types of lending
  • Getting the right data sets is crucial for assessing affordability and improving financial inclusion, to generate accurate financial offers for the right people and delivering good customer outcomes
  • Using data to support customer needs and ensuring responsible loan decisions is crucial
  • Establishing future affordability is a difficult equation, but an ongoing dialogue with data and customers is key to creating a picture of affordability that evolves over time
  • Using data to identify sharp changes in costs or affordability, while also having a longer term picture, gives consumers support throughout the lifetime of their product and can lead to better outcomes for the consumer
  • Using data, you can analyze changes in customer behavior such as increased number of loans, credit card usage, and overdrafts, to determine financial resilience and potential financial difficulties
  • Simplifying the affordability process through ongoing checks, by incorporating transaction data and other sources can allow lenders to become more outcomes-focused for their customers
  • Investing in technology to enrich transaction or credit file data and paint a more accurate picture of the consumer only may not be the best way to win customers over. Resources should be invested in ensuring good outcomes to the consumer through excellent customer support or ongoing care journeys
  • There is a lack of standardized view across the industry regarding good or bad customer outcome in affordability assessment
  • Investing in data and understanding will help provide better outcomes to customers, and is the future of the market
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Session 3: Regulations and changes in lending

  • The industry is polarized around the readiness for the implementation of Consumer Duty, with some seeing it as a radical shift requiring fundamental questions and others seeing it as simply treating customers fairly with a different badge
  • The impact of consumer duty on customers should be positive, with better transparency and outcomes for them
  • Innovation in finance will come from this in new firms, while some established firms may not see the need to innovate and will suffer consequences from lack of innovation
  • Challenge will be with evidencing adherence to principles-based regulation and the subjectivity of compliance
  • Changing your mind as evidence changes is however entirely rational even if others view it differently. This however will still be a challenge with Principles based regulation
  • Consumer Credit Act: The consultation process is underway involving different organizations including the treasury and FCA to determine the rules and regulations for customer engagement, communication and more in the consumer credit sector
  • Increasing regulation adds friction to the Buy Now Pay Later journey, creating tension withing the business model. Interesting to watch
  • Complaints coming from people who have never had a loan with a particular company are a significant concern seen with CMCs
  • Regulation around AI is inevitable due to the increasing complexity of algorithmic instruments which no human mind can grapple with. Over complexity and not understanding how things work fully is one of the biggest contributing reason to financial volatility in the last 15 years
  • The potential negative impact of AI and machine learning in areas like HR recruitment and lending to certain communities should be carefully considered. It needs to be regulated to ensure fair and unbiased practices
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Session 4: Digital Lending

  • There is increased interest in digital lending from both consumers and new lenders coming to the market, as well as existing lenders adopting more digital processes to improve efficiency and margins.
  • The remaining retail banks still have a branch network, but there is a lot of emphasis on digital focus and distribution channels
  • The lending decision process outside of the application process easier with good affordability. Someone who has a decent credit score and not looking to borrow too much or for an obscure or a purpose, will be easy to automate. Digital adoption however still has some inefficiencies that can make affordability assessment difficult
  • In Ireland, the Irish government changed legislation to cap interest rates at a 23% APR for consumer and personal lending. This will reduce net interest margins and therefore the level of risk that banks can take on, making credit markers less available for some people
  • It is going to be a challenging for near prime lenders and clients. The regulator is getting involved in pricing
  • Open finance is a huge opportunity, but consumer adoption is a challenge due to non-uniform approach and the need for value exchange, which in many cases is still not clear
  • Clients are hesitant to share too much information with banks and have concerns around over-sharing personal information via bank statements. This poses a challenge to get clients to be more open to the benefits of open banking and open finance
  • Digital lenders will have to adopt manual processes to ensure financial inclusion is achieved. This is a challenge that has not been looked at strongly enough by many digital lenders when they were setting up
  • Machine learning algorithms can make lending decisions based on customer’s background, career field, and propensity to repay the loan rather than just credit history
  • The urgency for completion of mortgage cases is causing lenders and brokers to adapt and find ways to give quicker answers
  • Data-driven lending decision is being used up-front instead of underwriting, making the two-step process more streamlined


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