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Are AI Credit Scoring Models a Solution to Human Bias
AI

Are AI Credit Scoring Models a Solution to Human Bias?

Ever wondered if a decision was made ‘fairly’, based on facts, rather than a gut feeling or incorrect assumptions? This is human bias, otherwise known as cognitive bias, and it is a subjective reality wherein decisions are made based on systematic errors in human thought. Many assume with the introduction of technology, such biases would be immediately eradicated, but this is often far from the case. However, AI scoring models can help reduce bias in decision-making, but only if done correctly. Let’s take a look at the challenges and benefits of solving loan and credit score problems using an AI approach.

What Are Cognitive Biases?

Cognitive biases are present with us all the time and influence the decisions we make and how we think. For example, they are the judgments that we make about people or things, the filter of how we see the world. Often, such biases are a survival tool and helpful in living our everyday lives, even if they are incorrect.

However, when it comes to organizational decision-making, they can be harmful and lead to incorrect decisions, and influence business strategy. Let’s get through some common biases briefly.

Confirmation bias

Putting additional ‘weight’ behind opinions or statistics that agree with your view. For example, you may have read an article that a particular category of borrowers is riskier than others, even if this is not the case. Such biases may be disproved with technology such as machine learning (ML) and data testing.

Status quo bias

As humans, we often expect the same events to keep repeating — the status quo. However, finances don’t work this way, and past events are not always indicative of future market conditions. Instead, using smart technology, such as AI loan approval software, can analyze the current market conditions and the client’s creditworthiness to give actual statistics about loan risks and repayment rates.

Bandwagon effect

Think it’s just day traders jumping on the latest market trends? It’s not, and it’s also likely your team could fall victim to the bandwagon effect at some time or another. In the lending industry and fintech in general, it can be very tempting to try to keep up with the latest trends — POS lending, buy now and pay later loans, etc. However, these can be counterproductive for your business strategy. Instead, focusing on your overall business strategy and the tools you need is always a smart path forward.

Risk-averse bias

Depending on the current market you are working in — bull or bear — and your company’s risk strategy, you may find your lending strategy to be too risk-averse or, worse, not risk-averse enough. Downturns in the market can lead to pessimistic thinking and management staff putting too much stock (belief) into the negatives. This is risk-averse bias. Conversely, by utilizing smart software tools, such as no-code AI software, it’s possible to take away the fog of risk and make it clear for everyone.

Is AI the Cure to Biases or the Poison? AI Discrimination Bias

As all these biases are human and stem from neurological thought processes, it would be easy to believe that adding a bit of tech would eliminate them forever. This is far from the case. In fact, when moving from traditional tools to AI several issues can appear:

  • Faulty algorithms. For AI algorithms to function efficiently, they require precise questions to be coded into the algorithm. This means that from the very beginning, the algorithm has to be calibrated correctly to ensure fairness in lending.
  • Lack of context. Just as the algorithm has to be correct to function properly, so too does it have to have the context to do so. As ‘fairness’ occurs in a bubble — lending provider’s demographic, communities, etc.— it has to meet the definition of fairness for this grouping. Having too wide or narrow a concept can lead to miscalculations.
  • Problems with processes. Machine learning and AI credit scoring models need to be trained in order to work effectively. Failure to complete this step or rushing to release a lending product before it’s ready can lead to unintended bias.
  • Determining what fairness is. Should everything be 50/50? Is it fair to lend to more women than men if they show better results in a creditworthiness check? Should 90% of your applicants be from a specific background? Is my organization dealing with credit fairly? All these questions and more will arise as you define what credit fairness means in your organization. However, it’s important to note that no matter the answer, it must always fall within regulations for fair lending in your jurisdiction.
  • Dealing with unknown values. It may come as a surprise, but many AI algorithms are only discovered to hold bias when tested. For example, think of Microsoft’s Tay chatbot that turned racist in a day, or Amazon’s biased algorithm, which accidentally discriminated against women. This is why it’s important to test and uncover unknowns that may impact the algorithm and reduce issues at the early stages.

But why exactly is overcoming credit bias such an issue? Aaron Klein, Senior Fellow in Economic Studies at the Brookings Institution, suggests that reducing bias augments the dynamics of credit-giving and could combat the issues of generational wealth.

Data Can Become the Main Source of AI Discrimination Bias

For AI to work effectively, it requires data that is accurate, clean, and usable. However, not all data fits the bill. The majority of data that an organization acquires or has access to is raw, mostly unusable data, and can unintentionally mislead the algorithm into making incorrect decisions.

This is why, when engaging an AI solution, a data cleaning step is always included as standard. Having clean, accurate data gives your organization the best chance of delivering a valuable service to your clients while reducing business risk.

Moreover, the poor data can hit your pocket too. It’s estimated that between $9.7 million to $14.2 million is lost each year due to bad data. For data to work effectively and for bias to be reduced, it’s important to:

  • Clean the dataset. Reducing errors at the initial step is important to ensure that the AI credit scoring models can work effectively.
  • Build smart models. No model is perfect from the first time around. Test your models to ensure they are not biased.
  • Collect data smartly. You don’t need all the information about your client and the market. In fact, this may confuse your AI tech, making it ineffective. Instead, implement processes that carefully gather the right types of data you need to carry out your creditworthiness check or other lending decisions.

How to Make AI Solutions Work for You?

When it comes to creating an AI credit scoring solution for your business. The rule of thumb is similar to that of a healthy human diet — what you ‘feed’ impacts the results. While a poor diet can result in a reduced lifespan, poor data can result in an unusable solution. Here’s the key element of ensuring the AI does what you want it to do.

Establishing what you want the AI to do

Ok, you want a low-risk decision-making model that only adds to your loan portfolio — great! However, AI doesn’t work like that. First, you need to frame the challenges and questions into algorithms that answer specific questions.

Not: Is my client creditworthy?

Yes: Does my client’s creditworthiness fall between X and Y parameters, acceptable by my organization?

Essentially, you need to translate a subjective question into a quantitative one that allows for fairness. For example, factors you can take account of within this include:

  • What’s my client’s current expendable income?
  • Does my company need to issue a higher number of loans or issue higher-ticket loans?
  • What are the profit margins?
  • Which other criteria does a client need to have?
  • Etc.

However, it’s important to remember that all this has to be done fairly. For example, algorithms can intentionally or accidentally be created to deliver on predatory loans (payday loans, for example) that may be considered discriminatory behavior.

Collecting data, the right way

Where are you getting the information about your lenders and ideal credit partners? This is the question you need to ask when implementing an AI model for credit. Issues with data can occur at any stage of the lending process. However, it is exceptionally common for them to creep in at the early stages. There are two issues with this.

  • Initial data is not representative of the potential audience. For example, say you want to roll out your lending services across the US, but your sample contains a large number of male lenders with an income of $120K+ from New York. This will automatically bias your data set.
  • Relying too much on historical lending decisions. When past lending behavior automatically predicts future decision-making, it can automatically bias the algorithm to act upon human decisions. That’s why it’s important to test and review results throughout and after the implementation process of any AI credit scoring models.

Undertaking proper data preparation

Data is king when it comes to your AI credit scoring models. That’s why preparing it the right way with due diligence is the only way to get the results you need. This is where data preparation and cleaning come into play. At this stage, it’s important to ensure that:

  • the data you have is the data you need
  • it’s understood correctly by the algorithm.

For example, unrepresentative data can cause just as much of a problem as data that is overweighted. For example, attributing one’s gender to poor creditworthiness or geographic location. By preparing the data correctly, such instances can be reduced at the early stages of the AI loan approval software process.

The Role of the Human in AI Credit Scoring Models

So far, we have assumed humans to be biased creatures, unable to make fair decisions, but just like AI isn’t perfect, so too do humans have a role to play in ensuring fairness when building a usable AI solution for credit scoring.

Unlike AI and machine learning, humans can effectively deal with nuances or variables in lending that machines cannot. Human judgment is required at all stages when implementing AI technology to ensure it functions effectively and is unbiased. But what if human thinking itself is subjective?

  • Reviewing results and making connections. Although AI and ML are effective at getting results, they lack the consciousness to consider if their behavior is defined as fair or good. In this respect, humans are best placed to test and evaluate if the results delivered by the AI credit scoring models are meeting the criteria or if nuances have started to appear. If this is the case, the algorithm may need to be adjusted to ensure fairness.
  • Framing the algorithm within current law. AI lending solutions do not exist in a vacuum. While current legal frameworks haven’t quite caught up with the advances of tech, this doesn’t mean they do not hold some helpful ‘advice’ or ‘guidance’ for what is wrong and right when using AI lending solutions. Humans can utilize their skills to adjust and frame AI within current laws and practices to ensure it meets standards.
  • Considering fairness and ethics. Legal practices are helpful to ensure compliance. However, fairness isn’t just law. By evaluating the effectiveness of the solution regarding ethics and other factor and asking ‘critical questions’ human, representatives can tackle bias issues before they gain traction. For example, it may help reduce bias against people that recently changed their job but have a good overall employment history in loan underwriting or reduce the down payment amount if they borrow has had a consistently significant salary.

Can AI Itself Be Used to Reduce AI Bias in Lending?

Although it may sound counterproductive, the latest AI technology can play its own role in reducing AI decision-making bias. Alongside humans, AI can be used to highlight and determine biases already existing in an organization and ones that have appeared in the algorithm.

How? Often, AI acts as an amplifier for decision-making. It creates rules based on algorithms and data, which it holds to be true. If these are biased, this should become obvious fairly quickly, provoking the algorithm to be adapted and upgraded.

At the same time, specific algorithms can also be deployed to actively target bias in lending. For example, as males represent 80% of venture capital — a well-known statistic — this can be used to create more fairness in the algorithm by adding more weight to other characteristics when it comes to issuing loans.

Creating a Perfect AI Credit Scoring Solution for Your Business

No AI solution is perfect the first time around. But that is no reason to avoid the technology completely. Instead, the best step forward is by engaging in AI tech with the help of low-code AI solutions and reliable providers that are experienced in the nuances of reducing the impact of human bias on AI. For example, AI credit scoring technology, such as GiniMachine, can be onboarded by businesses to take care of their credit scoring needs using smart AI. The system’s models are trained to help reduce bad loans and take into account a wider range of alternative data to ensure lending fairness.

Interested in GiniMachine for AI credit scoring? Get in touch with the team to know more.

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