Availability of consumer credit on reasonable terms is an important element of a well-functioning economy. Access to affordable credit allows households to smooth their cash flows and overcome the ups and downs in their financial lives. This might entail the purchase of important goods and services to support their livelihood (e.g. replacing a car used to travel to work) or to maintain a good standard of living (e.g. buying a new washing machine).
"A confluence of regulation, technology developments and market factors means that there is a huge opportunity for a new entrant that can serve these market segments effectively"
The problem in the UK is that many people do not have the ability to borrow at reasonable rates when they need to. Over 15 million people in the UK are unable to access affordable finance and end up borrowing at excessive rates from high cost lenders, or not at all. Still more consumers (one in four) borrow persistently high amounts using their credit cards, revolving their balances with no clear repayment plan, incurring the high interest rates that typically result from borrowing in this form.
Why does this happen? There are a few reasons for this. A couple are:
Covid-19 makes this worse. Many people have suffered significant disruption to their income, or face a more uncertain future. Whilst many have resumed or will soon resume work, the period of reduced income will have left many with unpaid bills and/or unmet needs and with worries about what they have to face in the future. This means that the supply of affordable credit is more important than ever before.
Unfortunately, this coincides with a significant reduction in affordable credit supply (also see Fintern paper "Bridging the Lending Gap" ), as traditional lenders seek to "de-risk", hindered by their failure to discriminate between good and bad responsible lending decisions. Credit decisions are often based on backward-looking credit scoring techniques, which only serve to highlight the obvious (there has been a recent problem) and do not consider current developments and trajectory.
What is needed is a forward-looking approach that takes into account each customer's specific circumstances and their recent history. Traditional lenders appear unable or unwilling to invest the required infrastructure or change their processes to fill this gap.
This is where Fintern comes in. Fintern believes there is a real opportunity to serve consumers better by taking a fairer, more sustainable and more customer-centric approach.
This approach to lending is underpinned by Fintern's values, summarised as:
"We will always charge a fair and sustainable interest rate and we will never seek to profit from customer misfortune, inertia or poor decisions"
Fintern will make responsible lending decisions based on a rigorous and comprehensive analysis of the customer's affordability and existing financial commitments, achieved by using Open Banking data in a streamlined digital experience. It also means charging a transparent and reasonable amount of interest (initially we anticipate an interest rate of 18.8% p.a.) over a medium term of borrowing (between two to three years) for amounts ranging from £1,000 to £10,000. Fintern's approach will leave room for lender profit, but it does not exploit the customer or trap them in an endless and expensive cycle of revolving credit.
In return for our providing access to their accounts through Open Banking, we offer our customers a uniquely satisfying customer experience and degree of flexibility, such as the option to take payment holidays during the term, and the ability to change the repayment amount or to repay the loan without fees or penalties. Provided that the customer can afford to, then we aim to offer as much flexibility as possible, for example if the customer wants to take a payment holiday to support a one-off purchase or an unexpected expense. Where such a feature impacts the cost of borrowing, we always show this very clearly and plainly. There is never any APR trickery.
We provide valuable insight to the customer by leveraging their Open Banking and credit bureau data and to help them to minimise their total cost of borrowing. Improving customer's financial capability and delivering sustainable and affordable credit are core values that are consistently expressed through our customer promises and policies. By creating a better customer experience and achieving better long-term customer outcomes, Fintern will simultaneously minimise credit losses, build trust with the consumer market and maximise brand value.
Our policies turn traditional behavioural incentives or 'nudges' upside-down. Typically, fees and charges are intended to support lender profitability. Take prepayment fees for example. At Fintern, there are no fees for early repayment or overpayment. Faster repayment reduces total cost of borrowing; this is good for the customer; therefore, we do not penalise it by charging a fee.
We will always charge a fair and sustainable interest rate no higher than the maximum rate offered by high street banks and never at the levels of payday lenders or credit cards. The are no hidden charges or APR cliff edges. We will never seek to profit from customer misfortune, inertia or ignorance. Finally, if we can detect signs of customer vulnerability, we will take proactive steps to support and will immediately discontinue any actions that could aggravate the customer's situation.
We believe that in the long run, putting customers first will lead to the trust and brand value that delivers rapid growth and a sustainable and profitable lending business.
"The algorithm we use involves an element of machine learning, but incorporates a bigger dose of common-sense rules"
Fintern makes use of advanced technology and data science to deliver our service. At the heart of the Fintern decision-making process is a detailed analysis of affordability based on an analysis of Open Banking transaction-level data on income and expenses. This leads to an assessment of the customer's affordability or "ability to pay" based on the amount of income remaining after they have paid all their bills. On top of this we consider additional credit signals, which provide additional information on a customer's "propensity to pay", as well as indicators of potential customer vulnerability. Let's look at these in turn.
Our affordability assessment is not complicated in its basic design, but does need to be detailed in its application. The algorithm we use involves an element of machine learning, but incorporates a bigger dose of common-sense rules. The latter is a defining feature of the Fintern IP and USP.
The basic idea is to:
The MAMP sets a ceiling for the realistic monthly affordability for a loan repayment, based on an estimate of likely income and expenses and an allowance for discretionary spending. The MAMP estimate is adjusted based on our level of confidence in the calculation based on the granularity and consistency of the cash flow analysis.
The MAMP concept provides a simple intuitive way of quickly summarising affordability. The customer then simply choose how much they are willing to pay each month – an amount no greater than the MAMP – and the system then works out immediately how long the borrowing term needs to be in order to pay off the loan.
There are challenges: the level of information about a transaction available through Open Banking data is not necessarily precise enough, especially for "bank transfers" transactions, where there is no unique identifier for target or destination accounts. This means that the "transaction description" (text field) needs to be leveraged in order to classify transactions, such as informal sources of income. Such descriptions often arbitrarily change over time, so there is an element of fuzzy matching required. Fintern has developed IP around how to do this effectively.
In addition, it is useful to assess the stability and frequency of transactions that have been identified as relating to a common type or counterparty. We can also look at overall income/spending deficits and other trends over time that give a sense of the sustainability of the customers situation. All of the above feeds into our responsible lending decision.
Our affordability analysis is not limited to Open Banking data. Traditional Credit Reference Agency (e.g., Experian, Equifax) data plays a key role in getting a holistic understanding of the customers situation, such as historical credit behaviour, arrears on other credit facilities, utilisation of credit cards, etc. For example, in our monthly allowance for (non-Fintern) financial Commitments, we substitute minimum payments to credit card providers with term-loan equivalents to ensure that we leave the customer with room to pay off their credit card balances.
Two customers with the same affordability (MAMP) can behave differently in practice. That is, they can have different propensities or likelihoods of paying vs. going into arrears/default. This simply reflects the fact that people are different: they have different knowledge, attitudes and values; a different state of well-being; different exposure to external shocks; and likely differential cost / benefit of not repaying the loan and suffering the credit and legal consequences.
Fintern's "credit signals" method for detecting this additional dimension of credit risk is based on a mixture of historical behavioural information and customer profiling based on Open Banking data. We note that this is not a simple one-size-fits-all approach, which is why we do not exclude customers with historical defaults, CCJs or Bankruptcy, so long as their forward-looking affordability and other indicators are broadly positive.
Open Banking data offers infinite possibilities for customer segmentation and this is an additional area where machine learning techniques can be useful, as it can readily capture the complex, non-linear and rapidly evolving nature of the data. Sometimes the presence of a certain factor can change the value of relevance of other factors, which is something traditional credit scorecards struggle with.
However, such indicators are usually intuitive. For example, regular charitable donations are an example of a positive credit indicator. Rarely do people in financial difficulty, or intending to defraud, continue to donate to charity.
Customer segmentation is not only about credit. High or increasing volumes of gambling or unhealthy lifestyle indicators could be an indication of higher propensity to default, but also an indicator of potential customer vulnerability.
We continuously monitor our customers' affordability, credit and vulnerability signals so that we can be more proactive in responding to requests for flexibility or in proactively offering support if it seems there is an early warning sign of a problem. This is not done in an intrusive way and initially takes the form of an in-app message offering context relevant resources for help and an invitation to discuss with one of our representatives if they feel comfortable to do so.
The use of such indicators for monitoring and in credit decisions requires care and consideration. Algorithms have limitations that need to be carefully managed. Our goal is to move toward an algorithmic 'triaging' process. This means many cases will result in 'auto-accept' and 'auto-decline' decisions, but we recognise that there will always remain grey areas and sensitive cases where a referral will be necessary, and customers will be able to request a manual review of an automated decision.
"Traditional lenders treat customers as a set of faceless and homogeneous agents to "risk manage" and to be profited from"
Many companies, not just Fintern, are creating innovative applications to realise the value of Open Banking data. Several popular Personal Financial Management apps use Open Banking data to help customers to aggregate and organise their finances and to offer price comparison services for regular outgoings such as utilities. Banks are (slowly) starting to use it for income verification and there are other firms emerging that, along with Fintern, plan to use it to support underwriting decisions. Like many ideas, it's the execution, the customer proposition and the brand that are more important than the technology itself.
Fintern believes there is a fundamental problem with the consumer credit market that goes way beyond the sophistication of credit decision algorithms. Traditional lenders treat customers as a set of faceless and homogeneous agents to "risk manage" and to be profited from, irrespective of whether this is at the expense of the customer outcome and often with unclear terms and conditions, and with numerous traps that can lead to the customer paying more than they expected.
Fintern seeks to deliver customer insight through our ability to aggregate and organise a customer's information. Fintern's core mission is to provide simple lending products at fair rates based on the values of transparency, sustainability and responsible lending.
Effective use of machine learning, technology and Open Banking Data are necessary but do not guarantee success in launching a consumer lending business. The Fintern approach is differentiated by our values and our customer-centric approach.
[1] The TUC estimated in 2019 that 4.7 million working-age adults had worked for an online platform at least once a week. See https://www.tuc.org.uk/news/uks-gig-economy-workforce-has-doubled-2016-tuc-and-feps-backed-research-shows
[2] Experian estimated in 2018 that 5.8 million people in the UK are invisible to the credit reference agencies https://www.experianplc.com/media/news/2018/britain-s-unseen-problem-58-million-people-invisible-to-the-financial-system/
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Availability of consumer credit on reasonable terms is an important element of a well-functioning economy. Access to affordable credit allows households to smooth their cash flows and overcome the ups and downs in their financial lives. This might entail the purchase of important goods and services to support their livelihood (e.g. replacing a car used to travel to work) or to maintain a good standard of living (e.g. buying a new washing machine).
"A confluence of regulation, technology developments and market factors means that there is a huge opportunity for a new entrant that can serve these market segments effectively"
The problem in the UK is that many people do not have the ability to borrow at reasonable rates when they need to. Over 15 million people in the UK are unable to access affordable finance and end up borrowing at excessive rates from high cost lenders, or not at all. Still more consumers (one in four) borrow persistently high amounts using their credit cards, revolving their balances with no clear repayment plan, incurring the high interest rates that typically result from borrowing in this form.
Why does this happen? There are a few reasons for this. A couple are:
Covid-19 makes this worse. Many people have suffered significant disruption to their income, or face a more uncertain future. Whilst many have resumed or will soon resume work, the period of reduced income will have left many with unpaid bills and/or unmet needs and with worries about what they have to face in the future. This means that the supply of affordable credit is more important than ever before.
Unfortunately, this coincides with a significant reduction in affordable credit supply (also see Fintern paper "Bridging the Lending Gap" ), as traditional lenders seek to "de-risk", hindered by their failure to discriminate between good and bad responsible lending decisions. Credit decisions are often based on backward-looking credit scoring techniques, which only serve to highlight the obvious (there has been a recent problem) and do not consider current developments and trajectory.
What is needed is a forward-looking approach that takes into account each customer's specific circumstances and their recent history. Traditional lenders appear unable or unwilling to invest the required infrastructure or change their processes to fill this gap.
This is where Fintern comes in. Fintern believes there is a real opportunity to serve consumers better by taking a fairer, more sustainable and more customer-centric approach.
This approach to lending is underpinned by Fintern's values, summarised as:
"We will always charge a fair and sustainable interest rate and we will never seek to profit from customer misfortune, inertia or poor decisions"
Fintern will make responsible lending decisions based on a rigorous and comprehensive analysis of the customer's affordability and existing financial commitments, achieved by using Open Banking data in a streamlined digital experience. It also means charging a transparent and reasonable amount of interest (initially we anticipate an interest rate of 18.8% p.a.) over a medium term of borrowing (between two to three years) for amounts ranging from £1,000 to £10,000. Fintern's approach will leave room for lender profit, but it does not exploit the customer or trap them in an endless and expensive cycle of revolving credit.
In return for our providing access to their accounts through Open Banking, we offer our customers a uniquely satisfying customer experience and degree of flexibility, such as the option to take payment holidays during the term, and the ability to change the repayment amount or to repay the loan without fees or penalties. Provided that the customer can afford to, then we aim to offer as much flexibility as possible, for example if the customer wants to take a payment holiday to support a one-off purchase or an unexpected expense. Where such a feature impacts the cost of borrowing, we always show this very clearly and plainly. There is never any APR trickery.
We provide valuable insight to the customer by leveraging their Open Banking and credit bureau data and to help them to minimise their total cost of borrowing. Improving customer's financial capability and delivering sustainable and affordable credit are core values that are consistently expressed through our customer promises and policies. By creating a better customer experience and achieving better long-term customer outcomes, Fintern will simultaneously minimise credit losses, build trust with the consumer market and maximise brand value.
Our policies turn traditional behavioural incentives or 'nudges' upside-down. Typically, fees and charges are intended to support lender profitability. Take prepayment fees for example. At Fintern, there are no fees for early repayment or overpayment. Faster repayment reduces total cost of borrowing; this is good for the customer; therefore, we do not penalise it by charging a fee.
We will always charge a fair and sustainable interest rate no higher than the maximum rate offered by high street banks and never at the levels of payday lenders or credit cards. The are no hidden charges or APR cliff edges. We will never seek to profit from customer misfortune, inertia or ignorance. Finally, if we can detect signs of customer vulnerability, we will take proactive steps to support and will immediately discontinue any actions that could aggravate the customer's situation.
We believe that in the long run, putting customers first will lead to the trust and brand value that delivers rapid growth and a sustainable and profitable lending business.
"The algorithm we use involves an element of machine learning, but incorporates a bigger dose of common-sense rules"
Fintern makes use of advanced technology and data science to deliver our service. At the heart of the Fintern decision-making process is a detailed analysis of affordability based on an analysis of Open Banking transaction-level data on income and expenses. This leads to an assessment of the customer's affordability or "ability to pay" based on the amount of income remaining after they have paid all their bills. On top of this we consider additional credit signals, which provide additional information on a customer's "propensity to pay", as well as indicators of potential customer vulnerability. Let's look at these in turn.
Our affordability assessment is not complicated in its basic design, but does need to be detailed in its application. The algorithm we use involves an element of machine learning, but incorporates a bigger dose of common-sense rules. The latter is a defining feature of the Fintern IP and USP.
The basic idea is to:
The MAMP sets a ceiling for the realistic monthly affordability for a loan repayment, based on an estimate of likely income and expenses and an allowance for discretionary spending. The MAMP estimate is adjusted based on our level of confidence in the calculation based on the granularity and consistency of the cash flow analysis.
The MAMP concept provides a simple intuitive way of quickly summarising affordability. The customer then simply choose how much they are willing to pay each month – an amount no greater than the MAMP – and the system then works out immediately how long the borrowing term needs to be in order to pay off the loan.
There are challenges: the level of information about a transaction available through Open Banking data is not necessarily precise enough, especially for "bank transfers" transactions, where there is no unique identifier for target or destination accounts. This means that the "transaction description" (text field) needs to be leveraged in order to classify transactions, such as informal sources of income. Such descriptions often arbitrarily change over time, so there is an element of fuzzy matching required. Fintern has developed IP around how to do this effectively.
In addition, it is useful to assess the stability and frequency of transactions that have been identified as relating to a common type or counterparty. We can also look at overall income/spending deficits and other trends over time that give a sense of the sustainability of the customers situation. All of the above feeds into our responsible lending decision.
Our affordability analysis is not limited to Open Banking data. Traditional Credit Reference Agency (e.g., Experian, Equifax) data plays a key role in getting a holistic understanding of the customers situation, such as historical credit behaviour, arrears on other credit facilities, utilisation of credit cards, etc. For example, in our monthly allowance for (non-Fintern) financial Commitments, we substitute minimum payments to credit card providers with term-loan equivalents to ensure that we leave the customer with room to pay off their credit card balances.
Two customers with the same affordability (MAMP) can behave differently in practice. That is, they can have different propensities or likelihoods of paying vs. going into arrears/default. This simply reflects the fact that people are different: they have different knowledge, attitudes and values; a different state of well-being; different exposure to external shocks; and likely differential cost / benefit of not repaying the loan and suffering the credit and legal consequences.
Fintern's "credit signals" method for detecting this additional dimension of credit risk is based on a mixture of historical behavioural information and customer profiling based on Open Banking data. We note that this is not a simple one-size-fits-all approach, which is why we do not exclude customers with historical defaults, CCJs or Bankruptcy, so long as their forward-looking affordability and other indicators are broadly positive.
Open Banking data offers infinite possibilities for customer segmentation and this is an additional area where machine learning techniques can be useful, as it can readily capture the complex, non-linear and rapidly evolving nature of the data. Sometimes the presence of a certain factor can change the value of relevance of other factors, which is something traditional credit scorecards struggle with.
However, such indicators are usually intuitive. For example, regular charitable donations are an example of a positive credit indicator. Rarely do people in financial difficulty, or intending to defraud, continue to donate to charity.
Customer segmentation is not only about credit. High or increasing volumes of gambling or unhealthy lifestyle indicators could be an indication of higher propensity to default, but also an indicator of potential customer vulnerability.
We continuously monitor our customers' affordability, credit and vulnerability signals so that we can be more proactive in responding to requests for flexibility or in proactively offering support if it seems there is an early warning sign of a problem. This is not done in an intrusive way and initially takes the form of an in-app message offering context relevant resources for help and an invitation to discuss with one of our representatives if they feel comfortable to do so.
The use of such indicators for monitoring and in credit decisions requires care and consideration. Algorithms have limitations that need to be carefully managed. Our goal is to move toward an algorithmic 'triaging' process. This means many cases will result in 'auto-accept' and 'auto-decline' decisions, but we recognise that there will always remain grey areas and sensitive cases where a referral will be necessary, and customers will be able to request a manual review of an automated decision.
"Traditional lenders treat customers as a set of faceless and homogeneous agents to "risk manage" and to be profited from"
Many companies, not just Fintern, are creating innovative applications to realise the value of Open Banking data. Several popular Personal Financial Management apps use Open Banking data to help customers to aggregate and organise their finances and to offer price comparison services for regular outgoings such as utilities. Banks are (slowly) starting to use it for income verification and there are other firms emerging that, along with Fintern, plan to use it to support underwriting decisions. Like many ideas, it's the execution, the customer proposition and the brand that are more important than the technology itself.
Fintern believes there is a fundamental problem with the consumer credit market that goes way beyond the sophistication of credit decision algorithms. Traditional lenders treat customers as a set of faceless and homogeneous agents to "risk manage" and to be profited from, irrespective of whether this is at the expense of the customer outcome and often with unclear terms and conditions, and with numerous traps that can lead to the customer paying more than they expected.
Fintern seeks to deliver customer insight through our ability to aggregate and organise a customer's information. Fintern's core mission is to provide simple lending products at fair rates based on the values of transparency, sustainability and responsible lending.
Effective use of machine learning, technology and Open Banking Data are necessary but do not guarantee success in launching a consumer lending business. The Fintern approach is differentiated by our values and our customer-centric approach.
[1] The TUC estimated in 2019 that 4.7 million working-age adults had worked for an online platform at least once a week. See https://www.tuc.org.uk/news/uks-gig-economy-workforce-has-doubled-2016-tuc-and-feps-backed-research-shows
[2] Experian estimated in 2018 that 5.8 million people in the UK are invisible to the credit reference agencies https://www.experianplc.com/media/news/2018/britain-s-unseen-problem-58-million-people-invisible-to-the-financial-system/
