Some Ethical Considerations in AI-Driven Bank Statement Analysis

Artificial intelligence (AI) is set to become an integral part of the financial landscape, including balance sheet analysis for decision-making. AI-powered solutions can help lenders improve efficiency by reducing processing time and increasing productivity.

While its integration offers numerous benefits, the use of AI in the financial domain also raises some ethical concerns like the inherent bias in the AI models and the challenges regarding data security, accountability and transparency.

A 2022 PWC report revealed that 61% of the industry respondents had regulatory concerns regarding privacy and data protection and 48% about the accountability of using AI adoption in the BFSI domain.

So, how do financial institutions (FIs) balance efficiency and fairness when using AI to make decisions?

Ethical Considerations in AI-Driven Bank Statement Analysis

Here are four aspects to consider when it comes to ethical considerations for using AI in the balance sheet analysis:

1. Bais and Discrimination

AI algorithms rely on the data used to train them; if biased data is used, these systems will inadvertently perpetuate the biases, leading to unfair conclusions.

Careful attention during data selection, model development, and ongoing monitoring helps mitigate biases and promote fairness in AI applications.

Using debiasing techniques and promoting diversity and inclusion within AI development teams encourages diverse perspectives and helps prevent prejudiced outcomes based on race, gender, or socioeconomic status.

Bank statement analysis can help lenders evaluate creditworthiness fairly and transparently and not contribute to discriminatory lending practices.

2. Transparency, Explainability and Accountability of AI-made Decisions

Another challenge with AI models is the lack of transparency in the decision-making process, often called the “black box problem”. The term refers to AI systems in which complex algorithms and deep learning are used and whose internal workings are not transparent or easily understandable to humans.

AI models should have decision-making processes that can be understood and audited; transparency in AI systems helps foster trust.

Those relying on bank statement analysis for their decisions should be able to offer their customers insights into how AI models arrive at their conclusions. This empowers individuals to understand the rationale behind their loan application being rejected or accepted.

3. Data Security and Customer Consent

Data security and privacy are some of the biggest challenges financial institutions face. AI relies on copious amounts of data for analysis, which raises concerns regarding privacy and data protection.

Safeguarding customer’s personal information is crucial for all stakeholders. Balance sheet analysis focuses on a broader range of data points, underscoring the need for robust data privacy mechanisms.

AI development also necessitates informed user consent mechanisms, especially when access to sensitive financial information is concerned. There should be systems in place that ensure respect for individual privacy and that collected data is used as per the relevant privacy laws and regulations.

4. Human Intervention

Even with artificial intelligence taking over data analysis and automating tasks, human intervention remains indispensable. FIs must have clear roles and responsibilities for human intervention in the AI-aided decision-making process.

For example, if the balance sheet analysis raises some red flags, an officer should be able to decide whether to seek additional or alternative data or reject the application outright.

Human intervention ensures that AI is a decision-making aid, not a replacement for human judgment and ethical considerations.

AI-Driven Bank Statement Analysis: How Can FIs Balance Efficiency and Fairness?

Efficiency

As a transformative force revolutionising the financial landscape, here’s how AI can help enhance efficiency:

1. Automated Data Extraction

AI systems use innovative techniques like Optical Character Recognition (OCR) and Natural Language Processing (NLP) to extract and interpret financial data from bank statements. These help lenders reduce the time and effort required to assess the loan application. Automation also reduces the chances of error and contributes to improved efficiency.

2. Rapid Analysis and Reporting

AI algorithms help FIs sift through vast data caches and quickly categorise transactions, identify patterns, and generate reports. Lenders can make better decisions based on this analysis without compromising on speed or performance inconsistencies, leading to faster decision-making and improved customer satisfaction.

3. Risk Profiling and Anomaly Detection

AI helps create more comprehensive risk profiles by analysing transaction patterns and detecting anomalies that may indicate fraud. This proactive approach aids lenders in improving their ability to mitigate potential losses while also streamlining the assessment process.

Fairness

Here’s how lenders can deal with the ethical challenges when employing AI in their processes:

1. Bias Mitigation

As noted above, one of the primary concerns when using AI models in banking is the potential for algorithmic bias and discrimination.

Apart from diversity and inclusivity, FIs must regularly update and purge the data to maintain its relevance and accuracy. These measures ensure that AI systems do not perpetuate discrimination based on race, gender, or socioeconomic status.

For example, a data scientist may train an AI model to automatically move an applicant to the final stage of the approval process if their credit score is above a certain threshold. However, they may ignore under-banked candidates or those with a shorter credit history but otherwise qualify for a loan.

Tools like bank statement analysis and GSTR analysis can help lenders get a more comprehensive view of the applicant using alternative data. This way, using AI-powered bank statement analysis can help promote financial inclusion.

2. Transparency and Explainability

Transparent AI tools help foster customer trust. Providing clear explanations for the criteria used in credit assessments and risk profiling can help stakeholders understand the logic behind AI-generated outcomes.

3. Regulatory Compliance

FIs must ensure that their AI systems comply with existing regulations governing credit reporting and consumer protection.

These must comply with regulations regarding the collection, use and sharing of consumer credit information to ensure that consumers’ credit information is accurate and used appropriately.

4. Human Oversight

While AI can enhance efficiency, human oversight is vital as it takes care of unintended consequences and governance issues and helps address them proactively.

According to reports, the Deputy Governor of the RBI highlighted the necessity of human supervision in AI-driven decisions to address complex or ambiguous cases and ensure ethical considerations are not overlooked.

Organisations must have policies to determine who is responsible for the outcome of AI systems.

To Sum It Up

AI is helping lenders improve the quality of their decisions and reduce errors and frauds, at the same time, it allows customers to access customised and user-friendly solutions. Balancing the ethical concerns with the benefits of AI can help create a more inclusive, safe and robust financial landscape.

Finezza offers a suite of AI-driven products including the Bank Statement Analyser that helps improve productivity by 8x and reduces processing time by 5x without compromising data safety.