Conversational AI chatbots are transforming industries by streamlining customer service, automating transactions, and enhancing user engagement. However, evaluating these systems remains a challenge, particularly in financial services, where compliance, user trust, and operational efficiency are critical. This paper introduces a novel evaluation framework that systematically assesses chatbots across four dimensions: cognitive and conversational intelligence, user experience, operational efficiency, and ethical and regulatory compliance. By integrating advanced AI methodologies with financial regulations, the framework bridges theoretical foundations and real-world deployment challenges. Additionally, we outline future research directions, emphasizing improvements in conversational coherence, real-time adaptability, and fairness.

When users receive either a positive or negative outcome from an automated system, Explainable AI (XAI) has almost exclusively focused on how to mutate negative outcomes into positive ones by crossing a decision boundary using counterfactuals (e.g., "If you earn 2k more, we will accept your loan application"). Here, we instead focus on positive outcomes, and take the novel step of using XAI to optimise them (e.g., "Even if you wish to half your down-payment, we will still accept your loan application"). Explanations such as these that employ "even if..." reasoning, and do not cross a decision boundary, are known as semifactuals. To instantiate semifactuals in this context, we introduce the concept of Gain (i.e., how much a user stands to benefit from the explanation), and consider the first causal formalisation of semifactuals. Tests on benchmark datasets show our algorithms are better at maximising gain compared to prior work, and that causality is important in the process.

Structured finance, which involves restructuring diverse assets into securities like MBS, ABS, and CDOs, enhances capital market efficiency but presents significant due diligence challenges. This study explores the integration of artificial intelligence (AI) with traditional asset review processes to improve efficiency and accuracy in structured finance. Using both open-sourced and close-sourced large language models (LLMs), we demonstrate that AI can automate the verification of information between loan applications and bank statements effectively. While close-sourced models such as GPT-4 show superior performance, open-sourced models like LLAMA3 offer a cost-effective alternative. Dual-agent systems further increase accuracy, though this comes with higher operational costs. This research highlights AI's potential to minimize manual errors and streamline due diligence, suggesting a broader application of AI in financial document analysis and risk management.

FinTech lending (e.g., micro-lending) has played a significant role in facilitating financial inclusion. It has reduced processing times and costs, enhanced the user experience, and made it possible for people to obtain loans who may not have qualified for credit from traditional lenders. However, there are concerns about the potentially biased algorithmic decision-making during loan screening. Machine learning algorithms used to evaluate credit quality can be influenced by representation bias in the training data, as we only have access to the default outcome labels of approved loan applications, for which the borrowers' socioeconomic characteristics are better than those of rejected ones. In this case, the model trained on the labeled data performs well on the historically approved population, but does not generalize well to borrowers of low socioeconomic background. In this paper, we investigate the problem of representation bias in loan screening for a real-world FinTech lending platform. We propose a new Transformer-based sequential loan screening model with self-supervised contrastive learning and domain adaptation to tackle this challenging issue. We use contrastive learning to train our feature extractor on unapproved (unlabeled) loan applications and use domain adaptation to generalize the performance of our label predictor. We demonstrate the effectiveness of our model through extensive experimentation in the real-world micro-lending setting. Our results show that our model significantly promotes the inclusiveness of funding decisions, while also improving loan screening accuracy and profit by 7.10% and 8.95%, respectively. We also show that incorporating the test data into contrastive learning and domain adaptation and labeling a small ratio of test data can further boost model performance.

In this article, we will answer the main questions about the new phenomenon in the world of IT - MLOps, what challenges those who have decided to put work with data on stream and want to put the MLOps approach into practice will have to face, as well as trends and what awaits machine learning technology in the future. I suppose there is no need to explain who DevOps is and what machine learning is. The news is that recently these concepts have been combined. As usual: it is easier to manage expectations, results, and iterations for clients; compare the results of experiments; recheck hypotheses, and implement developments. Of course, all these advantages do not arise by themselves.

An important part of a data scientist's role is to explain model predictions. Often, the person receiving the explanation will be non-technical. If you start talking about cost functions, hyperparameters or p-values you will be met with blank stares. We need to translate these technical concepts into layman's terms. This process can be more challenging than building the model itself. We will explore how you can give human-friendly explanations. We will do this by discussing some key characteristics of a good explanation. The focus will be on explaining individual predictions.

Data analytics is the process of analyzing raw data in order to draw information and make conclusions about the data. Data analytics is an important field in data science because it helps businesses optimize their performance. Data analytics helps businesses reduce costs and increase the overall efficiency of a business. When a bank receives a loan application the bank has to make a decision regarding whether to go ahead with the loan approval or not. The bank makes a decision on the loan based on the applicant's profile.

Is there a line connecting machine learning observability to explainability, leading to responsible AI? Aporia, an observability platform for machine learning, thinks so. After launching its platform in 2021, and seeing good traction, Aporia today announced a $25 million Series A funding round. Aporia CEO and co-founder Liran Hason met with VentureBeat to discuss Aporia's vision, its inner workings and its growth. Hason, who founded Aporia in 2019, has a background in software engineering. After a five-year stint in the elite technological unit of the Israeli intelligence forces, he joined Adallom, a cloud security startup that was later acquired by Microsoft.

The news: UK regulators have signaled that they will clamp down on artificial intelligence (AI) use in banking that might be used to discriminate against people, per the FT. Banks which use AI to approve loan applications must be able to prove the tech will not worsen discrimination against minorities. The bigger picture: AI is a significant growth area in banking. Its market size is projected to soar globally from $3.88 billion in 2020 to $64.03 billion in 2030, with a CAGR of 32.6%, per a Research and Markets report. AI in banking is maturing, and as data analysis improves, it brings the potential for more accurate decision-making.