Machine Learning Enables Inclusive Access To Financial Services

 Technology in financial services can be somewhat of a double-edged sword. On one side, new technological innovations, like artificial intelligence (AI) and machine learning (ML), are striving to make financial products and services more available by making it easier to identify customers, expedite credit approval, and improve access to financial services for all. On the other side, if the proper precautions are not taken, companies in the industry might be using data to power AI applications that hold intrinsic biases, which work against efforts to make technology more inclusive and accessible.

Why Human Bias Persists

Most fraud prevention models, and most AI models in general, rely on large sets of historical data to drive machine decision-making. However, historical data is susceptible to the inherent human biases of the real world, too, as evidenced when a U.K. passport office photo checker showed a bias against dark-skinned women.

In financial services, when AI is built accurately and inclusively, businesses are enabled to onboard a more inclusive customer base, accounting for marginalized groups that were prevented from using new financial products due to historic biases and policies that are no longer relevant today. Onboarding these new groups helps banks build more diverse datasets, in turn helping their AI models become more diverse and accurately identify correct transactional behavior among their customer bases.

What does this mean for future AI?

The intersection of data science and financial inclusion is where model bias inherently lives. Models are trained to match their training set as closely as possible, and model bias can be accidentally coded into the data behind these AI and machine learning applications. Model bias is clearly demonstrated in autonomous vehicles, more often spotting white pedestrians than black ones, which raises serious safety and discrimination issues and concerns about the viability of autonomous vehicles overall.

Model bias can come in less obvious forms as well and can be hidden in all kinds of AI applications. Unfortunately, while these applications are intended to grow access to financial services, model bias can be discriminatory against traditionally marginalized and underrepresented groups. Model biases make it difficult for people to acclimate to new environments. When I first came to the U.S., finding a credit line of even $100 was difficult. Because I had no history of credit in the U.S., I was likely labeled as a high-risk client, despite no evidence to indicate that was actually the case. This, unfortunately, happens to countless others and should be fixed by working to mitigate inherent biases and incorporating diverse, inclusive datasets.

When it comes to leveraging AI to help stop fraud and financial crime, the traditional data-generating process for purchases and payments is influenced by human-made rules, limits and controls imposed over decades. Data can be drawn from specific demographic profiles of cardholders and merchants over time, which can lead to bias in how those demographic groups are allowed to transact moving forward. For example, people might be blocked from transacting simply based on their previous, tangentially related cases of bad credit or other financial factors. With fair models, financial institutions can provide inclusion to groups of individuals that might have been excluded due to biases in the original data.

How To Mitigate Bias

If financial institutions want to prevent unwanted outcomes in payments and fraud prevention, they must be able to recognize uncharacteristic patterns in people’s spending behaviors. Many fraud prevention tech companies track suspicious behavior in order to suss out and flag fraud and financial crime. However, it’s more important to track normal behavior so they know when something is out of line.

Understanding what genuine behavior looks like for each person can make it easy to spot unusual behavior, but it can also be misinterpreted to make broad assumptions when it comes to a specific demographic’s financial risk if left unchecked. When something is flagged, banks should also work to understand why it was flagged and if the reason is justified or based on intrinsic bias. Algorithms that offer an explanation for their decision-making are useful here because they allow operators to scrutinize the model’s logic. This process of continuous learning and retraining helps mitigate the risk of bias or discrimination and allows for fresh data to be introduced to AI algorithms in real time.

What Needs To Happen

As the use of data becomes more prevalent alongside the use of AI and machine learning, we have started to see governments around the world dabble in regulating this beast of an innovation. However, for the most part, there is a lack of regulation, which means individual companies are responsible for their use of fair models to promote inclusivity. Fair models not only create more inclusivity in the financial ecosystem, but they also offer more insight into behavior that can identify whether financial transactions are authentic or potential sources of fraud. AI and ML, as our own research recently indicated, provide the key to driving down financial fraud and keeping customers and their finances safe.

It’s important that individual companies commit to inclusivity in their development and deployment of AI and machine learning applications—and equally important that consumers play their part in advocating for more advanced fraud prevention to protect themselves, their families and the global financial community. Everyone should have fair representation in data and fair access to equal fraud prevention and protection.

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Article resource: https://www.forbes.com/sites/forbestechcouncil/2022/11/18/machine-learning-enables-inclusive-access-to-financial-services/?sh=31a4915839e2