This paper explores the integration of Explainable Automated Machine Learning (AutoML) in the realm of financial engineering, specifically focusing on its application in credit decision-making. The rapid evolution of Artificial Intelligence (AI) in finance has necessitated a balance between sophisticated algorithmic decision-making and the need for transparency in these systems. The focus is on how AutoML can streamline the development of robust machine learning models for credit scoring, while Explainable AI (XAI) methods, particularly SHapley Additive exPlanations (SHAP), provide insights into the models' decision-making processes. This study demonstrates how the combination of AutoML and XAI not only enhances the efficiency and accuracy of credit decisions but also fosters trust and collaboration between humans and AI systems. The findings underscore the potential of explainable AutoML in improving the transparency and accountability of AI-driven financial decisions, aligning with regulatory requirements and ethical considerations.

Beliefs and values are increasingly being incorporated into our AI systems through alignment processes, such as carefully curating data collection principles or regularizing the loss function used for training. However, the meta-alignment problem is that these human beliefs are diverse and not aligned across populations; furthermore, the implicit strength of each belief may not be well calibrated even among humans, especially when trying to generalize across contexts. Specifically, in high regret situations, we observe that contextual counterfactuals and recourse costs are particularly important in updating a decision maker's beliefs and the strengths to which such beliefs are held. Therefore, we argue that including counterfactuals is key to an accurate calibration of beliefs during alignment. To do this, we first segment belief diversity into two categories: subjectivity (across individuals within a population) and epistemic uncertainty (within an individual across different contexts). By leveraging our notion of epistemic uncertainty, we introduce `the belief calibration cycle' framework to more holistically calibrate this diversity of beliefs with context-driven counterfactual reasoning by using a multi-objective optimization. We empirically apply our framework for finding a Pareto frontier of clustered optimal belief strengths that generalize across different contexts, demonstrating its efficacy on a toy dataset for credit decisions.

Artificial intelligence (AI) has been able to enhance the ability for us to leverage a larger volume of data generated in everyday business activities, and the FinTech industry is no exception. Financial services have enabled many individuals to break free of the traditional bank, expanding their options tremendously. The use of AI in FinTech can be used to optimize customer experiences, minimize risk and fraudulent transactions, and improve consumer credit decisions. Customers are constantly looking for more personalized experiences with their FinTech companies. One way many companies are accommodating this is through artificial intelligence.

In its most recent advisory, the CFPB addressed a critical question – "When creditors make credit decisions based on complex algorithms that prevent creditors from accurately identifying the specific reasons for denying credit or taking other adverse actions, do these creditors need to comply with the Equal Credit Opportunity Act's requirement to provide a statement of specific reasons to applicants against whom adverse action is taken? The answer is an obvious'Yes'. With the CFPB's circular reminding everyone of adverse action notice requirements under the ECOA Act, some credit unions find themselves in a quandary when it comes to explaining their credit decisions, which is perceived to be difficult when they use state of the art decisioning algorithms. However, modern AI solutions have moved beyond mere aspects of explainability to enable fair lending, and have gone the extra mile to remove inherent biases that may arise in data based models. Nonetheless, it is necessary to understand the CFPB's guidance and how AI can effectively be a solution itself. The use of algorithms in making lending decisions is not something novel or new. Credit risk assessment naturally requires getting your arms around as much relevant data as you can. A mix of models and algorithms have been the backbone of credit decisions for around 4 decades now, with credit analysts using financial statements, credit histories, and other data sources to estimate credit risk, set credit limits and recommend payment plans. With time, the datasets in question have become so voluminous that lenders had to move from manual methodologies to computational models for analysis of data using analytics. Recent advancements in computational methods have introduced the "AI" element in lending processes to make credit risk assessments much more accurate. Artificial Intelligence and Machine Learning models leverage a diverse set of alternate data sources beyond bureau, and use historical training data to determine non-linear correlations between data points, and provide advanced predictive signals on member behavior and lending outcomes. The unique proposition here is the ability of AI/ML models to analyze voluminous quantities of data, detect hitherto unknown correlations, and keep self-learning and adapting the models with little or no manual interventions. AI enabled technologies have helped put the spotlight on the increasingly visible disparities in existing lending processes. A 2019 paper by Robert Bartlett & Co. helps quantify this disparity: "Black and Latino applicants receive higher rejection rates of 61% compared to 48% for other races.

Consumer Financial Protection Bureau (CFPB) issues policy statement on credit decisions based on complex algorithms. On May 26, the CFPB issued Circular 2022-03, which addresses an important question about algorithmic decision-making: "When creditors make credit decisions based on complex algorithms that prevent creditors from accurately identifying the specific reasons for denying credit or taking other adverse actions, do these creditors need to comply with the Equal Credit Opportunity Act's requirement to provide a statement of specific reasons to applicants against whom adverse action is taken?" The Circular says yes, compliance with ECOA and Regulation B is required even if complex algorithms (including AI and machine learning) make it difficult to accurately identify the specific reasons for taking the adverse action. Further, the Circular makes clear that those laws "do not permit creditors to use complex algorithms when doing so means they cannot provide the specific and accurate reasons for adverse actions." White House executive order calls for study of predictive algorithms used by law enforcement agencies.

As credit markets continue to evolve, banks may take advantage of products which utilise machine learning – software which allows banks to anticipate risks more effectively. But should banks revise their credit risk management processes accordingly and employ these new solutions? According to McKinsey, AI and machine learning technologies could add up to $1 trillion in additional value to global banking every year. Financial institutions are using machine learning to make credit decisions more accurately and consistently while reducing risk, fraud, and costs. For example, Citi bank recently transformed its critical internal audit using machine learning--something that has contributed to high-quality credit decisions.

Through artificial intelligence (AI), the power of machines is leveraged to streamline banking operations. With AI, tasks such as data processing can be automated, which allows these tasks to be completed more efficiently and lightens the workload for bank employees. AI can also be used to draw conclusions from data, which has applications such as detecting fraud and making credit decisions. As a result, banks using AI technologies are able to handle high loads of data at a fast pace, while keeping bank employees' workloads at a more manageable level and reducing operating costs. Nevertheless, there are some drawbacks associated with the use of AI in banking, including job elimination, bias, and data privacy risks.

We were delighted to host three brilliant AI minds working in the financial sector to a panel on our AI in Finance and RegTech Summit stage last week. Speakers on this panel included Ronan Brennan, Strategy and Innovation Manager at NatWest, Mackenzie Wallace, Specialist in FinTech/RegTech at The World Bank, and David Bryan, Director of Presales at MANTA. The panel was broken down into the four themes listed below and was finished with a Q&A. MANTA also recently released a guide on how to achieve full compliance and build trust in the financial sector. Click here to access the free guide.

Artificial Intelligence (AI)-driven lending practices are gaining visibility and credibility. AI tools used with machine learning can analyze more data for a more accurate answer to loan requests. Lenders using new AI systems can evaluate bank account balances calculated with purchase history, social media habits, and utility payments to determine a person's creditworthiness. Those without established credit can benefit greatly from AI lenders. New startup lenders are using AI to approve personal loans for people with a short or non-existing credit history who have a reliable income and a high earning potential.

Artificial intelligence (AI) is revolutionizing the transformation of interaction with money, and how consumers and companies alike access and manage their finances. AI in finance encompasses everything from chatbot assistants to fraud detection and task automation. AI is helping the financial industry to streamline and optimize processes ranging from credit decisions to quantitative trading and financial risk management. The decision for financial institutions to adopt AI will be accelerated by technological advancement, increased user acceptance, and shifting regulatory frameworks. I've put together a rundown of how Artificial Intelligence can be used in finance leading the way.