Generally, people aged 31-50 are paying $160 more on food per person per month when compared to last year. What effects will this have on an average family Holiday dinner? From the Bureau of Labor Statistics, we compiled a typical basket of goods that a family may consume during the holiday season. The most recent data released is from September 2022. Given the volatile economic conditions, it is safe to assume that these prices will have fluctuated another 1-3% between then and the upcoming holiday season. First, let’s assume that the average holiday dinner consists of twelve people, assuming a family of four with invited relatives and friends. We have compiled a list of classic North American Holiday dinner items: turkey, mashed potatoes, fresh vegetables, condiments, ice cream, wine, and other miscellaneous items potentially used to make a wonderful dinner.

Based on these price increases, the total of a basic Christmas dinner is now $177.63, up from $156.94 or a ~13% total increase in cost. It is important to note that these are only the bare essentials of a holiday meal; if you begin to incorporate homemade cakes, pies, and other specific delicacies, costs can rapidly increase by another $50-100.

For families with young children, presents are the most critical and expensive part of the holiday season. As the years tick on, gifts are becoming even more costly than ever before. Even LEGOⓇ, the most basic of children’s holiday gifts, has increased by ~13% in the past year. This increase alone beats cumulative CPI by a landslide and puts families in a more difficult position than ever before. Many popular children’s toy manufacturers, including MGA, will produce shrunk-down toys at lower price points. The Bratz dolls producer is currently on track to make 200 toys this holiday season under $10, up from 15 toys last year. However, these more attractive price points come at the cost of quality and, as previously mentioned, size.

After almost three years of being stuck in their homes due to COVID-19, families are booking plenty of vacations to make up for it all. As with most things this year, they, too, have increased in cost by far the highest of all. Not only due to inflation but also due to the substantial increase in demand. If you’re visiting family out of state or province, you’ll be paying almost 33% more for your airfare this year than in 2019. But, costs quickly add up even more if you are on a tropical vacation. STR, a company that provides market data on the hotel industry, reports that the average room rate in the US has increased by 21.4% from 2019 to almost $153 a night. The Bureau of Labor Statistics CPI also indicates that restaurant prices have increased by 8% yearly. These factors create a dangerous situation for families looking to escape their hometown this holiday season. Will people still be able to afford their current lifestyle based on these crippling financial situations?

There is no doubt that the cost of this holiday season is going to be extreme for most families. As cost pressures increase across the board, lenders are facing an increased demand for credit while also facing volatile patterns in the economy that leave conventional credit assessment unreliable. Helping expand access to fair credit products, without increasing risk, will help set lenders apart this holiday season, supporting consumer liquidity and growing their businesses: having fair and accurate credit assessment methods, especially on historically underserved/underbanked people, will be critical to this mission.

���ϳԹ��� helps deserving people get the credit products they deserve, helping lenders Find Great Borrowers From Lead to Loan. With over 90 million credit invisibles across the US and Canada, ���ϳԹ��� is here to support a happy holiday season with financially inclusive and accurate credit insights from lead sourcing and screening to loan decisioning.

Have thoughts on the rising costs of the holidays and their impact on credit? Leave a comment below!

Over the last 40 years, the world has seen the average age of home buyers increase by a total of 16 years. This change has been the direct result of many different factors including declining marriage rates, increased student debt and increased desire of urban living. Marriage rates over the past 20 years have been in consistent decline as people begin to prefer a more open and individualistic lifestyle. A recent survey by The Institute for Family Studies found that almost 20% of people aged 25 to 50 are not married, up from 9% in 1970. National student debt has also reached an all time high, as it turns into a life-long mission to pay it off for some. In fact, student debt has increased 2.5x over the last 20 years to $1.75 Trillion in the US! Carrying student debt makes it much more difficult to purchase a home and is encouraging individuals to continue living at home for longer. This is exacerbated by the current trend of millennials and gen Z moving into dense urban settings which are saturated with expensive, small properties. Housing prices in the US have increased 393% since 1985, normalized to include inflationary increases. While north of the border in Toronto, the average home price in downtown currently sits around $1.2M compared to the suburbs which sit around $900k. The high price per square foot in the dense urban settings makes it infeasible for young adults to own these properties, forcing them to rent.

The trend of living in tight, urban settings is almost completely eliminating the need for a personal vehicle. This is yet another big purchase that people are holding off, further shrinking their credit file. Between 2000 and 2009, the average age of new vehicle buyers increased by almost six years and in the US, only 60% of 18 year olds are now license holders, versus 80% in 1983. Also, the recent shift towards ride sharing apps such as Uber and delivery apps such as UberEats, there has become less of a need to own a vehicle in the modern world.

These delays in home and vehicle ownership presents a unique challenge for lenders, as some of the most traditional forms of credit are disappearing from files. How are lenders able to properly score someone with such a thin file, potentially only a small $500 limit credit card? ���ϳԹ���s answer is through real-time, alternative credit scoring. Instead of using traditional data sources for credit reporting such as mortgages and car loans, ���ϳԹ��� inputs alternative sources into our AI/ML predictive models. Thousands of different data sources are aggregated into our state of the art system which generates a SixScore, our explainable and proprietary scoring system on a range from 300-850. Contact us today to find out how your company can benefit from adding alternative data to your underwriting.

1. Optimized customer acquisition costs and efficiency;
2. Substantially improved approval rates by connecting underwriting and marketing; and,
3. Higher average deal sizes and sustainable portfolio growth.

How It Works

Credit Bureau + is a platform designed to provide��FCRA-compliant decisioning from lead to loan. We can help you source a lead pool, or we can support your lead purchasing from third-party sources. Through soft-pull credit reports and alternative data reports, we can score and implement credit-based knockout rules that��align marketing with your underwriting strategy. Our platform generates automated buy/pass decisions and suggests possible loan amounts, terms, and rates, and arms your marketing team with a series of leads that are pre-qualified for a specific lending product. We also integrate into your existing workflows and are designed to��enhance your existing processes, and will��work with you and your marketing mail house, LOS/LMS, other partners for the most seamless experience possible.

���ϳԹ��� takes it a step further to develop a propensity score that can predict which of your leads are most likely to accept your loan terms, enabling��laser-focused direct marketing strategies that target your best leads to stop wasting valuable resources on unqualified or low-propensity prospects.

Want to see how we helped a leading US Installment Lender with this service? We successfully doubled their originations, boosted their approval rate over 90%, increased their average deal size by 35%, and offered a 2-5X ROI.

Read the Case Study

Compliance and Steering with Demographic Factors

The most common conventional segmentation variables are demographic factors: things like age, gender, location, family situation, income, and education. They are the easiest to access and generally can be quite effective for standard product marketing. In lending, however,��most of these factors are prohibited under the ECOA and similar legislation.

Even in marketing, lenders need to be careful on disparate impact, as��. When lacking compliant credit metrics to support segmentation efforts, marketers may rely on improper, and possibly non-compliant, correlations between demographic information and booked loans, resulting in different demographics receiving different promotional content (or no promotional content altogether). The non-compliant practice of��steering��is characterized by guiding customers to a sub-optimal loan product relative to what they would qualify for, made especially illegal if done on the basis of these prohibited factors, and this is a key activity that��marketers must avoid. Applying a��compliant credit-based lens in marketing, like Lead Decisioning by Credit Bureau +, enables lenders to reap the upside that marketers attempt to emulate, without missing Invisible Primes in other demographics and without facing regulatory exposure.

Steering Example:

Steering on the basis of explicitly prohibited demographic factors is an obvious use case; where one age group, gender, or similar clearly receives promotions of sub-optimal offer. However, lenders (and more specifically marketers in lending) should watch for factors that correlate with prohibited factors.

For example, consider hypothetical Neighborhoods A and B. Neighborhood A consistently receives promotional offers with lower amounts or higher rates compared to Neighborhood B, to the extent that residents of Neighborhood A are signing up for these offers, even though they would qualify for the offers in Neighborhood B. While this alone may already cause some regulatory trouble, Neighborhood A is also disproportionately comprised of one particular group protected under a prohibited factor (e.g., race, gender, age). Because of this, there is a disparate impact, where there is a demonstrable difference in exposure to certain offers along prohibited factor divisions. Furthermore, even if there is no harm done, in the sense that eligible Neighborhood A residents are able to find the Neighborhood B offer and sign on with that, regulators may still find the existence of a non-compliant steering practice.

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Conventional Credit is Blind

Between government support and lender forbearance, COVID-19 left a gap in reliable data on conventional credit files, with historically low default rates and an ironic rise in credit scores. Despite this, COVID-19 also represented a significant economic shock, with many losing jobs and relying on government support to make ends meet. Lacking reliable credit payment data has made it difficult to discern good credit risks from bad credit risks using traditional methods, especially in the subprime and near-prime space. With credit payment information and scores only now beginning to normalize, the lagging nature of conventional bureau data, combined with credit scoring models based on pre-Covid economics,�� means that financial institutions are missing out on using critical information that is happening now and accounting for new or emerging post-Covid financial relationships.

Volatility Destroys Conventional Models

Throughout the past decade or so prior to COVID-19, model-builders enjoyed a period of relative macroeconomic stability, which enabled them to construct models based on this period with the assumption that the overall environment would remain relatively stable. Since COVID-19, however, the economy has faced rapid and substantial changes that disrupt this underlying assumption. Changes to the macroeconomic environment logically implies that credit scoring models and inputs established pre-COVID cannot be assumed to have the same predictability on probabilities of default. Furthermore, these macroeconomic changes will have different effects on different individuals, meaning that the same set of potential inputs may have differing impacts on creditworthiness depending on the actual values of those inputs. With conventional credit data being a lagging indicator, as conventional credit models are redeveloped, validated and put into production, the pace of unforeseen economic disruptions (i.e. partial economic recover, ongoing inflationary impacts or new Pandemic virus’) will continue to challenge predictability.

Economic Factors Hitting Lower-Income Consumers Hardest, Others to Follow

Though savings increased when looking at the entire population over the pandemic, closer examination reveals that those with higher incomes (and higher credit scores) had the bulk of this saving: low-income households (bottom 40% of the population) and subprime consumers continued to struggle. For the subprime lender, being in-tune with their segment of the population is extremely important; the savings that economists suggest will help tie people over in the current volatility do not exist for those that were already most likely to be hit hardest by rising inflation and interest rates. As these conditions continue to persist, increased costs faced by prime rated customers higher on the credit quality ladder will deplete current savings cushions and potential stress disposable income levels to a point where negative credit migration and rising delinquency will catch lenders off guard. Savings accumulated during the pandemic, if any, have created a cushion for some consumers, but lenders should be prepared for a recessionary crash once the cushion depletes.

Inflation is Restricting Cash Flows: Defaults Incoming

Speculation surrounding an incoming recession notwithstanding, inflation alone has resulted in more of each paycheck being put towards basic costs of living like food and transportation, and less free cash flow to do things like pay back loans. Consequently, even though defaults have yet to rise to pre-pandemic levels, they are rapidly trending upward and are likely to rise significantly as limited savings begin to deplete. Furthermore, now-ending work from home arrangements are resulting in the resumption of costs associated with a return to the office, such as childcare and transportation.�� This also means that collections efforts will become increasingly difficult, and new originations that rely solely on conventional credit data or scores are operating without accounting for ultra-current data or new post-COVID economic relationships excluded from current conventional models acclimated to pre-COVID economies. Combining this with already thin-file or otherwise inaccurately scored consumers reveals a large intersection of consumers that are at-risk and difficult to safely lend to.

Auto, Variable Rate Loans Showing First Signs of Weakness��

According to data from the major bureaus, variable rate credit products are the first to falter, with auto loans showing an early uptick in delinquencies. Furthermore, asset-backed loans like automotive loans and HELOCs are additionally vulnerable to the economic impact on asset valuation. With over 30% of debt susceptible to variable rate increases, lenders now have another factor to consider when making creditworthiness decisions, especially when facing higher costs of capital.

No Historical Precedent with Data

While economists and press enjoy drawing parallels between the 1980s interest rate and inflation environment to today, for lenders, this holds little to no value. 40 years ago, the internet was just being born, data was few and far between, and the computing power we now enjoy on a smartwatch required rooms of mainframe computers. Consumer consumption expectations and the levels and composition of total consumer debt is materially different between the 1980s and today. Without the existence of this data, let alone having it in a format accessible and understandable by today’s methods and technology, formulating a risk adjudication model on the basis of a 40-year old precedent is impossible, and one could argue that even with that data, there’s sufficient differences that such a model would fail regardless.

Learning from the Present for the Future

In the absence of a viable historical precedent to build a model, lenders should be prepared to adapt and learn quickly from new information and data. Machine Learning (ML) models in production that continuously learn from new performance data and calibrate continuously are the best way to harness this volatility. ML platforms, such as the Credit Bureau+ platform, gain incredible predictive power through learning from volatile conditions and enable lenders to remain ahead of the curve using the most relevant data to instantaneously generate applicable predictive insights. While having models which continually adjust is not without it’s operational challenges, having updated credit scores at your fingertips produces optimal results regardless of the macroeconomic environment and permits quick strategic adaptation to business cycle changes. Combining this capability with thousands of alternative data points that are ultra-current and tuned to the most up-to-date view of the borrower yields highly predictive insights that will allow lenders to both weather the storm and safely grow their businesses.

Alignment with National/International Standards

Companies must comply with financial service industry regulations depending on their location and target market. Compliance with standard regulations improves credibility and prevents penalties. These compliance policies differ from country to country. For example, the US Fair Credit Reporting Act ensures fairness, accuracy, and customer data privacy. Compliance with such policies makes the company more trustworthy, thus, increasing the customer base and profits.

Personnel Awareness

The company should educate and train its employees on the repercussions of data breaches and cyberattacks. Technically educated employees watch for phishing attempts and emails that could risk their organization’s security—cloud-native platforms prevent employees from unauthorized access and sharing sensitive data. Hackers usually rely on ignorance and user error to leverage cyber attacks, but well-trained employees could minimize this risk. If the employees are familiar with the company’s privacy and data security policies, they have a higher tendency to act safely and cautiously while accessing company systems.

Penetration Testing��

Penetration testing or ethical hacking refers to a simulated cyber attack on your security systems to test for its vulnerabilities or weaknesses. In this test, the professional acts as a hacker to gain access to your systems. Penetration testing can reveal the strengths and weaknesses of the organization’s system, thereby conducting a full risk assessment. Dedicated teams can directly target detected weaknesses of the system and take appropriate measures to eliminate or reduce these vulnerabilities.

Access Management Systems

Access Management processes aim to provide system access to authorized users and restrict access for unauthorized users. They monitor which users have the permission to access what kind of files, systems, and services. An efficient access management system mitigates the risk of internal security threats and maintains a safe gateway to sensitive data. It ensures that employees only have access to the information required to perform their jobs. Access management systems can also automate access removal upon task completion so that there is no breach of data.

Basic Protection Controls

Basic protection control measures include defining the company’s most sensitive digital assets, privacy, and security policies to meet national/international compliance principles. The company should also take active steps to protect these assets, like providing end-to-end encryption, firewalls, and multi-factor authentication. Since FinTech companies handle sensitive personal and financial information of the customers, these features must be mandatory.

Business Continuity Management Systems��

Business Continuity Management measures the capacity of a company to function normally, maintain business operations and continuously deliver its product even after any disruptive incident. A business continuity management policy should integrate the principles of disaster recovery, business recovery, crisis management, incident management, emergency management, and contingency planning.

How ���ϳԹ��� is Successfully Overcoming Modern Data Security Challenges

���ϳԹ���®, a FinTech SaaS delivering Credit Bureau 2.0®/Credit Bureau +, complies with the Personal Documents Protection and Electronic Documents Act which governs the collection, use, and disclosure of personal information in commercial businesses. It is also compliant with the Credit Reporting Agencies’ legislation in the US and Canada and is working towards ISO 27001 certification to ensure best business practices.

To prevent data breaches, ���ϳԹ���® employs a Cyber Security team that specifically focuses on application, network, and system security. It also conducts background checks of all new hires to ensure credibility. Non-disclosure agreements and proper training ensure that staff is well versed in the organization’s security policies. These stringent measures keep sensitive data safe, making it one of the most reliable companies in the FinTech sector. Trusted third-party vendors regularly scan all networks (including test and production environment) to ensure that the company’s systems are robust and non-penetrable. It also maintains a documented vulnerability management program which includes periodic scans, identification, and remediation of security vulnerabilities on applications and infrastructure.

For data protection, ���ϳԹ���® protects confidentiality, availability, and accountability in access to assets while they are in the transition stage between storage and transmission. Its well-administered Asset Management policy includes identification, classification, retention, and disposal of information and assets. All data is encrypted using secure TLS cryptographic protocols. In case of a breach, all affected parties are directly notified to take appropriate steps. The resources of the company are only accessible through secure connectivity and require multi-factor authentication. It reviews access permissions quarterly; access is restricted to a need-to-know basis and revoked upon employees’ termination. This allows smooth access and maximum security of critical data. Services offered by ���ϳԹ���® are hosted from enterprise-class data centers managed by public cloud providers. It provides efficient failover resilient systems that maximize the availability of systems. With techniques such as data replication, ���ϳԹ���® ensures a speedy recovery and a continual delivery of services even during times of external disruption.

It is an inspiring and challenging time for FinTech companies, given the evolution of digital technologies; however, it comes with increased risks of cyber attacks and breaches. An investment in security measures will keep your company safe, productive and credible. ���ϳԹ���® is committed to protecting the data of its customers through the above-mentioned powerful features. To��learn more about how ���ϳԹ���’s secure systems and the measures we have taken to alleviate potential risks and threats, visit us at��

AI can harness large amounts of customer data��

AI technologies and tools can significantly improve the credit underwriting process. Lenders can accurately assess the borrower’s credibility by combining traditional customer data sources (such as credit/repayment history) with alternative information (like banking history). This combination makes credit-lending decisions more effective and accurate. This is particularly beneficial for auto-lenders as they can combine the car’s features and purposes to make the risk models more predictive. For example, the primary usage of the car — leisure, jobs like construction, or farm work — can give valuable insights into the customer’s financial standing and the potential depreciation of the vehicle. Therefore, AI models can be very effective to signal the risk of default.

The car’s features, including its model, year of manufacture, used status, number of current/previous owners of the car, primary usage, and the number of miles driven, can help determine the Loan to Value (LTV) risk cuts. These features are also useful to calculate the difference between the estimate and the actual sale price. ���ϳԹ���®,�� FinTech SaaS delivering Credit Bureau 2.0®/Credit Bureau +, identifies a larger pool of creditworthy customers with increased accuracy and insight into the probability of default, probability of delinquency, and ability to manage payback. It collaborates closely with clients in development and integration, providing significant improvements in the lift, stability, bad loan analysis, and return on investment.

AI can make the vehicle depreciation curve more accurate

A car loses its value the minute it leaves the showroom. During the first year, the average rate of depreciation is around 20%, after which it stabilizes to 15% per year each consecutive year. The rate of depreciation depends on the model, mileage, and region. The depreciation curve can be used in estimating the loss, i.e., the value that lenders can get at repossession vs. the value of the loan still withstanding. AI technologies can make the depreciation curve more accurate using actual resale data on loans, regions where the cars are used (example: snowy regions can cause more wear and tear), and the vehicle’s primary purpose. AI can build models that can induce automated loans based on depreciation curves and data analytics, thus, retaining financially stable customers. Besides this, AI models can identify customers trading their cars for new dealerships because their loan is greater than the car’s value. Using advanced AI analytics, auto-lenders can identify such customers and offer remedies, like new car purchases or new loans.

AI can reveal creditworthy Invisible Primes

The use of AI tools can open the auto-lending markets to many thin-file applicants called subprimes or invisibles. Post-2009 recession, there has been a shift in the auto-lending industry, lending more and more to subprime borrowers than ever. However, the lenders have given more debt to borrowers than their ability to pay. This puts such borrowers at the risk of losing their cars if they default on their loans. In such a circumstance, the use of AI in auto-lending decisions is more advantageous than ever. AI tools can effectively reveal “credit invisibles”/“unscorables” and evaluate their true creditworthiness by aligning traditional credit data with vast data from alternative sources like banking. Using AI in their underwriting decisions, auto-lenders can attract creditworthy subprime borrowers and reduce the probability of default.

���ϳԹ���® is an industry leader in its ability to use AI/ML models that grow with your business, harnessing numerous data sources to deliver meaningful, explainable, and fully compliant risk scores, even on those that were conventionally thought of as credit invisibles. We have a unique ability to score millennials who form the largest demographic segment of the auto market. Most of them lack credit history due to generational differences in credit use. Our AI/ML models collect massive amounts of consented consumer data to calculate an accurate credit score. For lending leaders who need to score these financially stressed or underbanked borrowers fairly and ethically, ���ϳԹ���® offers a fully compliant, data-driven, AI-powered solution. To learn more about how ���ϳԹ���® can help with your underwriting process, visit us at��

In the next three years, digital lending (using AI) is expected to double in size, reaching as high as 10% of the total loans in the US and Europe. The use of artificial intelligence has the potential to alter the status quo in the following ways substantially:

AI Can Avoid Traditional Reporting and Crediting Systems

AI can incorporate new forms of data like payment of rent, utility, telephone bills, consented banking history, loan data, court data, giving a more comprehensive picture of the borrower’s credit. In a similar vein, ���ϳԹ���®, a FinTech SaaS delivering Credit Bureau 2.0®/Credit Bureau +, is using AI to produce highly predictive credit scores by leveraging public data, proprietary data, and consented banking data. ���ϳԹ���’s cloud-based SaaS decision support platform processes these complex data sets to promptly deliver a fully compliant and explainable AI- and ML-powered score. ���ϳԹ���® is helping deserving people get the loans they deserve, free from the constraints of old data to eliminate much of the traditional subconscious lending biases that may have left lenders vulnerable to costly litigation while bringing substantial returns on investment.

Digital Footprint Analysis��

With a customer’s informed consent, AI can collect publicly available digital information like spending behavior, employment histories, and organizations customers belong to. The supplemental information produces a more accurate measure of their creditworthiness. ���ϳԹ��� ® uses patented algorithms to analyze publicly available digital information and consented consumer data, thereby collecting and analyzing thousands of data points beyond traditional bureaus.

Improving Risk-Adjustment Margins

In the increasingly volatile credit lending environment, lenders (especially short-term lenders) must make smarter underwriting decisions to remain profitable in the subprime, non-prime lending space. Lenders can increase profits and leverage assets more effectively by using AI software to assess the worthy borrowers and charge them an appropriate interest rate, allowing feasible repayments of the loan. The Credit Bureau + offered by ���ϳԹ���® uses a compliant system that generates scores based on data trifecta: lender’s data, customer’s data, and ���ϳԹ���® proprietary data that includes consented structured and unstructured publicly available data. Such data integration generates highly reliable credit scores. It allows lenders to improve loan inclusivity, expand loan originations, and grow business with confidence in their decision-making process.

AI in Loan Origination and Loan Management Systems��

Arguably, AI’s greatest asset is its versatility; its ability to be integrated into any system. AI, for example, can be integrated into loan origination systems (LOS) and loan management systems (LMS) as a way to enhance one’s long-term loan evaluation processes, or even improve their data access. These types of AI integrations, or add-ons, can help to significantly reduce processing time, operational costs, and the overall accuracy of customer (borrower) analysis. The latter is where AI’s potential with LOS/LMS systems truly lies.

Machine Learning (ML), a subset of AI, is still a black box concept for many organizations looking to enhance their decision-making through massive amounts of data analysis. Many financial institutions currently rely on LOS/LMS systems to help support their employees by providing additional ‘decision support.’ With the help of AI, or ML, lenders can improve the accuracy and fairness of their credit scoring techniques by creating highly informed evaluation models. Due to the speed and nature of AI, financial institutions can use AI to automate substantial amounts of the loan decision making process while also improving the accuracy, fairness, and consistency of the results. This is where ���ϳԹ���®’s value is fully on display.

Through its cutting-edge machine learning platform and exclusive dataset, ���ϳԹ���® enhances lender workflow wherever static, or standalone, LOS/LMS exist, by bringing additional “decision support” to the equation. With the ability to integrate into nearly any LOS/LMS platform, ���ϳԹ���®’s ML can leverage its growing, alternative data set to create models and scoring routines that avoid using prohibited factors and prioritize precision over anything else. Essentially, ���ϳԹ���® uses machine learning to automate the human Loans Officer, and provide detailed, ethical client (borrower) credit evaluations in a short amount of time and without personal biases. For LOS/LMS systems that are used by thousands of employees, this can be an extremely valuable resource for both the customer, employee, and company alike.

Elimination of Errors

The use of AI in LOS/LMS reduces the likelihood of human error in processing loan applications. AI can also create a management system that identifies customer behavior patterns when they are close to bankruptcy, reducing risk for lenders and maintaining a system of worthy borrowers who will continue in the credit lending economy. At ���ϳԹ���®, credit scoring machine learning models evolve based on patented methods to eliminate human error and automate decision-making processes. Business decisions are made based on continuously evolving new information rather than static data collected just once.

Furthermore, the outbreak of the global pandemic has led to a macroeconomic crisis in the US where people were unable to work and did not have regular incomes. This has proportionally increased the number who do not have access to affordable credit. Such an environment has made traditional credit scoring even more redundant due to the rising economic uncertainty. AI and machine learning can use proprietary data to predict borrowers’ ability and willingness to pay loans, thus making this technology more efficient and trustworthy during these perilous times. Traditional methods have lacked innovation, and therefore, were not able to adapt to this new economic environment. AI in credit scoring can lead to more inclusive lending models, offering the subprime and credit invisibles an opportunity to enter the credit market economy. Besides that, it will allow financial institutions to speed up their economic recovery by unlocking the potential and demand of the underserved borrowers.

To learn more about ���ϳԹ���’s powerful Decision System that empowers lenders to increase originations and decrease defaults, helping borrowers get the money they deserve,��

The traditional credit scoring system, which uses a 5C discriminant analysis to assess the creditworthiness of the borrowers, is based on five evaluation factors: lender’s role, capital, collateral, capacity, and environment. With the increasing number of borrowers and the expansion in the loan scale of financial institutions, the traditional credit scoring system has become redundant. Traditional scoring systems deny customers credit based on these factors without taking into consideration any extenuating factors like their history of payments of utility bills/rent or telephone bills. Furthermore, the pandemic has����an atmosphere of economic uncertainty, changing customers’ backgrounds and needs, which has exposed the weakness of traditional credit scoring systems. In such a changing environment, ML technologies come handy as they can process massive quantities of data and provide a more accurate and dynamic credit score. ML technologies have a range of benefits for financial industries, especially in the field of credit scoring. This includes:

Credit Risk Management

Traditionally, financial institutions have used linear, logit, and probit regression models to predict credit risk for capital investments, stress-testing, and internal risk management procedures. However, the introduction of ML technologies has changed the landscape of credit risk modelling. Many financial institutions, for instance, have started using ML to improve financial risk predictions. Studies have shown that ML can process large quantities of unstructured and alternative data to����by 19%, making ML models better than statistical models for financial distress prediction. While traditional credit risk assessment methods can measure the future default risk of the borrower customer based on their lending history and repayment characteristics, ML technologies use an extended amount of data to analyze the borrower’s default risk and its correlation with behavioral and other soft information (ideas and opinions). Thus, ML can significantly reduce the company’s cost of loan delinquency by giving early warning signs of expected default.

Given the worldwide shift to digitalization, consumer digital transactions provide a more holistic picture of their financial behavior. ML easily integrates and processes these complex and oftentimes unstructured data sets to build custom credit models that are more nuanced and provide a holistic picture of default risk associated with a borrower customer. Thus, ML models can better evaluate loan applications. Besides this, credit reporting agencies utilizing ML can provide more confidence to the lenders in their lending decisions. Alternatively, ML can also feasibly identify more risky borrowers who have been given credit previously. Explained simply, the integration of ML in your credit scoring system offers greater flexibility and adaptability to macro-level economic changes. ML has the ability to quickly learn and adapt to new data inputs that provides you a highly predictive credit score even during precarious economic times.

���ϳԹ���®, a FinTech SaaS delivering Credit Bureau 2.0®/Credit Bureau +, uses explainable machine learning models to leverage thousands of data points that accurately measure the borrower’s rates of default and delinquency. This eliminates the guessing game and allows lenders to make their lending decisions with confidence. The ability of ���ϳԹ���® to process alternative sources of information (like banking history, payroll sources, etc.) at once in a sophisticated ML scoring tool in real-time takes into account the business strategy of the lenders, enabling them to reach the best possible lending decision. ���ϳԹ���® uses dynamic ML models that change and adapt as new data becomes available for real-time assessment. For example, with the customer’s consent, ���ϳԹ���® can leverage the customer’s personal data – with updates as new as the previous day. Compared to traditional credit scoring systems that use sometimes month-old data, this is a dramatic difference in data relevancy. This gives lenders an accurate and up-to-date assessment of the borrower’s creditworthiness.

Elimination of Errors/ Reduction of Costs

According to a��, the main problem with traditional credit reporting is the frequency of material errors. More than one in five US customers have a potential material error in their credit file that makes them seem riskier than they actually are. With such inaccurate information, these borrowers get a higher interest rate, less favorable terms, or a complete rejection of their application. Trained ML systems reduce human error and lenders’ reliance on manually created financial models.

Traditional credit scoring is primarily based on stagnant and incomplete information of borrower data coupled with the biases (racial/gendered/age) of limited and selective data. With ML technologies, financial institutions have helped lenders and credit underwriters decrease human error and eliminate judgement. According to the����“BCG Model for the Impact of AI in the Financial Job Market by 2027”, the use of ML technologies will improve the efficiency of jobs by reducing an average of 2.4 hours per person per day in the same functional activity. Thus, the use of ML can reduce the working hours of financial sector industries by 27% by the year 2027,significantly improving efficiency and reducing labor costs in the financial sector. ���ϳԹ���®’s powerful AI/ML models alleviate risks by measuring and pinpointing errors that factor into credit decisions. This can reduce human errors that are caused based on traditional scoring techniques.

To conclude, the use of ML technologies in credit scoring will better indicate default and delinquency risks, eliminate human errors, and reduce costs in the long term. While choosing an appropriate company for credit lending, ensure that the company uses the latest technologies to provide an accurate assessment of the borrower’s default risk. To know more about how ���ϳԹ���® uses ML technologies to harness large quantities of proprietary and alternative data to calculate the credit risk of a customer, visit us at:��

Benefits to All Stakeholders��

The use of AI/ML technologies can be used to identify creditworthy borrowers who have been traditionally excluded from the credit system due to the lack of credit history (underbanked individuals, credit invisibles). AI/ML can use large amounts of additional alternative data (e.g. current income, employment history, and/or applicant-permissioned data from their mobile phone) to calculate the credit score of borrowers. Such data-driven methods allow wrongly-scored subprime borrowers to get credit, increasing the number and type of people and businesses that banks and other financial institutions can serve in a highly legal & ethical way. The traditional credit scoring system, for instance, labelled 50% of Americans as less-than-ideal borrowers. Worse, approximately 40% of those people are wrongly scored. Inaccurate, or limited, systems reduce the addressable customer base for conventional lenders, and it leads to unnecessarily high interest rates or no credit at all for large segments of the population. The use of AI/ML in credit score calculation, however, leads to superior loan origination, has shown to increase lender’s profits by upwards of 15%, and contribute to a better return on their assets, all while keeping the risk level relatively constant.

Dynamic Scoring��

AI/ML technologies are increasingly used in credit scoring to quickly adapt to environmental changes, such as interest rates, average household income, and other measurable factors. This method of dynamic scoring allows lenders to make consistently accurate decisions, regardless of macro-level factors. Conventional credit scoring models are very static, and they do not take into account drastic changes (like the COVID-19 pandemic). But with the use of AI/ML technologies hosted inside a purpose-built infrastructure, scoring models can be retrained extremely rapidly to account for changes in the macro environment and in consumers’ behavior.�� In this way, volatility & chaos (which are toxic to conventional scorecarding methodologies and even computerized regression-based modeling techniques) can be harnessed & converted into assets:�� AI/ML helps eliminate the “fog of change” and accurately assesses the creditworthiness of borrowers even in highly volatile market conditions.

AI/ML Can Reduce Risks��

Unlike traditional credit scoring systems, AI/ML has the capability to handle and process large quantities of data very quickly. Companies are using AI/ML systems to build both traditional and alternative data models. By leveraging the processing power of these ML-generated models, institutions, such as lenders, can make predictions based on a particular individual or scenario, such as evaluating a first-time borrower application. Coordinating these systems for the purposes of dynamic scoring, however, can be quite difficult without the right platform to operationalize these AI/ML-built models.

Arguably, the greatest risk for any lender is granting a loan to a client who cannot pay it back. The best way to eliminate this risk is by quantifying the risk of every client, or borrower, and identifying not only their creditworthiness but also the loan amounts at which the individual is likely to pay off the principal and its interest. Evaluating the riskiness of a borrower is the key to a successful lending business. With an accurate risk evaluation method, financial institutions can maximize the number of loans given to credit-worthy borrowers, and minimize the number of loans gifted to unreliable ones. AI/ML can leverage massive amounts of data to help create models that are highly informed and highly adaptable. The success of which, however, is limited by the data set you feed it.

AI/ML-built models are products of the data that was used to create them. The parameters and evaluation techniques of which are directly informed by what the AI has learnt. By introducing large alternative datasets, you are essentially providing the AI/ML with a more complete picture of the borrower and their profile. Therefore, by feeding the AI/ML more data, you can increase the amount of information the model has to create holistic, fair, and accurate credit evaluations. With an improved scoring and decision making process, lenders can avoid declining good applicants and avoid approving bad applicants.

Just because one utilizes a large amount of data to inform their AI/ML, doesn’t guarantee, however, that these borrower evaluations will be void of bias. In fact, the inclusion of prohibited factors and limited datasets can skew an otherwise objective process into a biased one.

Compliance

The traditional data used to calculate credit scores can be����or other historically marginalized groups, including single parents or low-income groups. If not handled properly, AI/ML systems can reproduce these biases in their assessment as well.�� AI/ML algorithms should be�� properly modelled and fully supervised. Data ethics training should be provided to data scientists to ensure that the data used complies with regulations that ensure fairness (like the�� Fair Credit Reporting Act) to outright avoid–not merely reduce–the use of prohibited factors.

Identifying said prohibited factors is not always so easy. According to the����legal doctrine, even if prohibited factors are not used, algorithms and evaluation processes may inherently perpetuate biases based on prohibited factors, which are explicitly non-compliant with the Equal Credit Opportunity Act (ECOA), Regulation B, and other similar regulations. Companies offering these kinds of automated scoring services must take part in and consistently pass regular and voluntary compliance checks, which screen extensively for biased factors and results. Not only will it improve the quality and size of their lending base, it will also avoid them having to incur massive fines from regulators like the Federal Trade Commission (FTC).

Using AI and ML to Power Fair and Equitable Credit Scoring

The credit market is growing rapidly, and the use of AI/ML in credit scoring systems can lead to more efficient and more accurate decisions. The potential of AI/ML is vast, which, if properly harnessed, can lead to a fairer and more inclusive credit lending sector. And if not, it can lead to wide misuse of personal and financial data of customers, reproduce additional biases and further marginalize certain populations.

Therefore, a company offering financial services must focus on its privacy policies,����and take measures for financial inclusion. The right company will build or buy its AI/ML systems in such a way that they are truly harnessing permitted & permissioned data�� to improve accuracy and efficiency of their lending.

Removing discriminatory credit scoring policies is not as easy, however, as simply identifying prohibited factors or integrating ML-built models. Legal doctrines like the Effects Test prove that, like humans, internal biases are deeper than surface-level judgement. Traditional systems subconsciously reinforce biases and inequalities that can manifest themselves in skewed credit scoring, which is non-compliant. Achieving compliance means utilizing alternative data and scoring platforms to fairly, accurately, and inclusively assess everyone and thus gain the trust of your customers, stakeholders, and relevant regulators.