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Do fintech companies need to worry about fair AI?

Do fintech companies need to worry about fair AI?

Over the past decade, both Fintechs and AI (Artificial Intelligence) have evolved from disruptive buzzwords to crucial components of the financial industry. With the promise of increased efficiency, improved customer experiences, and reduced costs, fintech companies have readily adopted AI technologies in their operations.

In fact, 83% of respondents to a recent NVIDIA survey of Financial Service Providers (FSPs) agreed that AI is essential to their company and the future of Financial Services. However, with any new technology comes concerns for ethical and fair use. The rise of big data and machine learning has brought attention to potential biases in AI algorithms that can lead to discrimination against certain groups of people.

This has raised the question: do fintech companies need to worry about fair AI?


How is AI used by fintech companies?

Fintechs use AI in various ways, including credit scoring, fraud detection, chatbots for customer service, and investment recommendations. These technologies have significantly improved the speed and accuracy of tasks that were previously manual and time-consuming. For example, traditional credit scoring methods used by banks rely heavily on historical data like a person's credit score or income.

AI algorithms can analyse a more extensive range of data in real time, such as social media activity or online purchasing behaviour, to assess creditworthiness accurately. Similarly, AI-powered chatbots can quickly and efficiently handle customer inquiries and complaints without human intervention. This saves time and resources for both the company and the customer.

Some of the more common uses of AI in fintech include:

  • Fraud detection: AI algorithms can analyse large amounts of data to identify patterns and anomalies that could indicate fraudulent activities. This helps prevent financial losses for both the company and its customers.
  • Robo-advisors: These digital platforms use AI to provide investment recommendations based on a customer's risk tolerance, financial goals, and market data. This makes investment advice more accessible and affordable for individuals.
  • Credit risk analysis: AI algorithms can analyse a vast amount of data to accurately assess a person's creditworthiness. This helps fintechs provide loans and other financial services to individuals who may not have qualified using traditional methods.
  • Investment analysis: Fintechs use AI to analyse market data and identify potential investment opportunities. This helps them make informed decisions and stay ahead in a constantly changing market.
  • Personalised banking: AI-powered technologies can analyse customer data to provide personalised financial products and services that meet their specific needs. For example, banks can use AI to offer tailored mortgage rates or insurance plans.

These are just some of the many ways fintech companies use AI to enhance their operations and provide better services to customers.

Understanding fair AI

Before delving into whether or not fintech companies should be concerned about fair AI, it's crucial to understand what fair AI means. Fair AI is the concept of developing and using artificial intelligence in a way that does not discriminate against individuals based on their race, gender, age, or other protected characteristics. It involves ensuring that AI algorithms are not biased and do not perpetuate existing societal inequalities. Fair AI strives to create equal opportunities and outcomes for everyone, regardless of their background.

The main issue with the expansive use of AI by fintechs is that algorithms can unintentionally inherent biases from the data they are trained on. For example, if a credit scoring algorithm is trained on historical data that shows a bias against certain groups of people, it will continue to perpetuate this bias when making credit decisions.

The number of individuals with the right skill sets to program, test, and audit AI algorithms is limited, meaning they, and their potential biases, have a disproportionate impact on the development and use of AI. This represents a genuine concern for fintech companies, as discrimination based on AI decisions could lead to significant reputational and financial damage.

At the same time, investment decisions are equally vulnerable to bias because of the limited number of AI experts available to create and scrutinise these algorithms. There is some concern that AI-powered investment decisions could result in another financial crash as algorithms exacerbate bubbles and make harmful decisions based on the financial bias of the programmers who built them.

Why is that required in fintech?

The use of AI in financial decision-making is rapidly growing more common but remains opaque to most customers. Using AI models in fintech raises several questions regarding fairness and transparency.

Fintechs have a responsibility to ensure that their AI algorithms do not discriminate against any group of people. Moreover, there are regulatory pressures for financial institutions to demonstrate diversity and inclusion practices within their business operations.

For instance, the UK's Financial Conduct Authority (FCA) published its Machine Learning in Financial Services report in 2019, outlining its expectations for firms using AI. These include the need to design, monitor and test algorithms for biases regularly. Regulation of this kind is rapidly evolving and is expected to increase as AI becomes more integrated into financial services. While the majority of regulation is currently voluntary, it is likely that compliance with fair AI practices will eventually become mandatory.

Embracing AI fairness to avoid compliance risks is not the only reason why fintech companies should be concerned about fair AI. Ensuring fairness in AI has tangible business benefits and contributes to building trust with customers. Fintechs can use their commitment to fair AI as a competitive advantage, attracting customers who care about ethical and transparent practices. Moreover, diverse and unbiased data sets lead to more accurate predictions, improving the overall performance of AI algorithms. 

As customers become increasingly aware and concerned about data privacy and security, fintechs prioritising fair AI practices will likely be perceived as more trustworthy.

Customer trust is essential in the financial industry, and fair AI can help fintechs build and maintain it.

Why should fintech companies care about fair AI?  

There are several reasons why fair AI is essential for fintech companies. These include:

  • Compliance: As with any industry, fintechs are subject to regulations and laws that govern their operations. In recent years, there has been a growing focus on the ethical use of AI in financial services. Failing to comply with these regulations could result in penalties and damage to the company's reputation.
  • Customer trust: Fair AI is crucial for building and maintaining customer trust. Customers want to know that their data is being used ethically and that they are not being discriminated against. Any breach of trust could result in customers taking their business elsewhere. 
  • Business success: Fintech companies strive to stay ahead of the competition and provide innovative solutions for their customers. Incorporating fair AI practices can help fintechs differentiate themselves in a crowded market, attract more customers, and increase business success. 
  • Social responsibility: Fair AI is not only beneficial for business but also reflects the company's social responsibility. By promoting equal opportunities and combating discrimination, fintechs can contribute to creating a fairer and more equitable society.

These compelling reasons make it clear that fintech companies should indeed be concerned about fair AI and take steps to ensure their use of technology is ethical and unbiased.

The consequences of biased AI decisions

There have been numerous cases where AI algorithms have produced biased outcomes, leading to negative repercussions for both companies and individuals. For example, in 2018, Amazon scrapped an AI recruiting tool after it was discovered that the algorithm had a bias against women.

Similarly, in 2019, Apple came under fire when it was discovered that their credit card algorithm discriminated against women by offering them lower credit limits compared to men with the same financial circumstances. The Correctional Offender Management Profiling for Alternative Sanctions (COMPAS) algorithm used in the US criminal justice system has also faced criticism for being biased against people of colour, resulting in unfair sentencing.

ChatBot Tay underlined the potential for AI to perpetuate biases and discrimination when it was shut down just 16 hours after its launch in 2016. The chatbot quickly began spouting racist and sexist remarks as a result of learning from negative interactions with users on social media. Perhaps the most telling example of the consequences of biased AI decisions is Joy Buolamwini's research on facial recognition systems. Her work highlighted that these systems are less accurate at identifying people with darker skin tones and can perpetuate racial biases.

This has real-life implications, as facial recognition technology is increasingly being used for surveillance and law enforcement purposes.

These examples demonstrate the significant impact that biased AI decisions can have on individuals and society as a whole. It is crucial for fintech companies to proactively address fair AI concerns to avoid such negative consequences.

What legislation is there to regulate AI?

Currently, very little. AI remains largely unregulated, with most legislation focused on data protection and privacy. The European Union has taken the lead in addressing AI ethics concerns with the AI Act, the world’s first comprehensive AI regulation. The Act outlines seven core requirements that AI systems must meet, including transparency, human oversight, and robustness. AI systems are categorised into low-risk, high-risk, and unacceptable-risk categories, with different levels of compliance requirements for each.

The unacceptable-risk category includes AI systems that impose social scoring by classifying people based on behaviour, socioeconomic status or personal characteristics. This, in particular, could significantly impact fintech companies that use AI to assess creditworthiness or make loan decisions, as these systems often rely on personal data and potentially perpetuate discrimination. Unfortunately, this regulation is still in the early stages of implementation and may take a few years to become fully enforceable.

The US has yet to implement any specific regulations for AI, but there have been calls for more comprehensive legislation. The Algorithmic Accountability Act aims to address bias and discrimination in AI systems, but it has not yet been passed into law. 

Other countries, such as Canada and Singapore, have also implemented guidelines for ethical AI use, but there is still a lack of comprehensive global legislation.

Because of the lack of specific regulations, NGO (non-governmental organisation) initiatives have emerged to promote responsible and ethical AI use, including:

These organisations provide guidelines, frameworks and resources for companies to ensure the ethical use of AI.

The development and implementation of legislation are critical to ensuring fair AI practices in the fintech industry. As technology continues to advance, it is essential for governing bodies to keep up with these developments and take proactive steps towards regulating AI use.

Ensuring fair AI in fintech

Fintechs can take several steps to promote and ensure fair AI within their organisations, including:

Diverse hiring practices

Fintech companies can start by promoting diversity within their workforce, including diverse backgrounds and perspectives. This can help to minimise unconscious biases that may be present in the data sets used to train AI algorithms.

Diverse hiring practices also have benefits beyond addressing AI bias, as studies have shown that diverse teams lead to more innovative and successful businesses.

Data transparency

Transparency in the data sets used to train AI algorithms is crucial for identifying and mitigating potential biases. Fintech companies should aim to provide a clear understanding of the data sources used and regularly review and audit their data for any inherent biases.

Data audits

Regularly auditing AI systems for any biases is essential to ensure fair decision-making. Companies should also have processes in place to address and correct any identified biases.

At the end of the day, any machine learning algorithm is only as good as the data it is trained on, making continuous monitoring and evaluation of data crucial to ensuring fair AI.

Human oversight and accountability

Including human oversight in the decision-making process is essential for promoting fair AI. This can include regularly reviewing and testing algorithms and providing explanations for decisions made by AI systems.

Having clear lines of accountability within organisations can also help to identify and address any biases that may arise.

Continuous monitoring and evaluation

Fintech companies should regularly monitor and evaluate their AI systems to ensure they are meeting ethical standards. This can include conducting bias audits, seeking feedback from diverse stakeholders, and addressing any issues that arise promptly.

Instead of blindly relying on AI systems, fintech companies should continuously assess and improve upon their use of these technologies to promote ethical decision-making.

Collaboration with experts in ethics and diversity

Collaborating with experts in ethics and diversity can provide valuable insights into potential biases within AI systems. These experts can also help fintech companies develop policies and procedures to promote fair AI practices.

By collaborating with experts, such as the Algorithmic Justice League and AI Governance Alliance, fintech companies can ensure they follow best practices and stay up-to-date with the latest developments in AI ethics.

By implementing these measures, fintech companies can work towards building a more ethical and equitable future for all.

Fair AI is not just about avoiding negative consequences but also an opportunity to create positive change and build a better society through technology.

Overall, it is crucial for fintech companies to prioritise fair AI and take proactive steps to ensure the ethical use of AI in financial services.

Building toward a fair AI future

Given the prevalence of AI used in the fintech industry, it is essential for companies to prioritise fairness and ethics in their use of these technologies. This includes not only implementing guidelines and regulations but also actively seeking out diversity and promoting transparency and accountability.

With AI having a potentially massive impact on the financial lives of individuals and society as a whole, it is critical for companies to take responsibility and work towards building a fair AI future. By doing so, we can create a more equitable and inclusive financial system that benefits everyone.

The development of ethical AI practices in fintech is an ongoing process that requires continuous evaluation, improvement, and collaboration between all stakeholders involved. However, with a concerted effort and commitment to responsible AI use, the fintech industry can lead the way in promoting and ensuring fair AI for all.

Chaser is dedicated to promoting ethical and responsible AI use in the financial industry and is committed to upholding transparency, accountability, and diversity in AI systems and continuously monitoring and evaluating best practices to ensure fair decision-making for all individuals.

For more information on how AI is used as part of Chaser’s credit control automation software, book a demo with an expert or visit the Chaser blog for more industry insights.


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