Artificial intelligence is fundamentally reshaping the financial services industry, moving the bedrock of lending from rigid, manual processes to dynamic, data-driven ecosystems. This transformation promises to enhance efficiency and democratize access to credit, yet it simultaneously raises critical questions about fairness, transparency, and accountability. As financial institutions navigate this new terrain, the challenge lies in harnessing AI’s power responsibly.
Bidipta Datta, a FinTech specialist with over a decade of experience in automation and cloud data engineering, has been at the forefront of delivering complex credit and loan products. Her work involves strengthening credit risk decisioning, automating collections, and implementing Open Banking workflows. Datta’s insights shed light on how the industry is leveraging AI to create more inclusive and intelligent financial systems while addressing the ethical guardrails necessary for sustainable innovation.
AI and democratizing finance
The integration of AI into financial services is driving a significant transformation in how capital moves through the economy. By analyzing vast amounts of alternative data, from spending habits to income patterns, AI can identify creditworthy individuals who might be overlooked by traditional credit models. This shift marks a move toward more equitable and accessible financial products.
“What excites me most is its potential to democratise finance — to make sophisticated financial services accessible to people and businesses that traditional banking often overlooks,” says Datta. This is achieved through the convergence of various AI capabilities, such as natural language processing for customer interfaces and predictive analytics for forecasting financial behavior. The industry has seen AI applied to a range of tasks, including fraud detection and risk management.
The evolution continues with the emergence of autonomous systems. “We’re also seeing the rise of Agentic AI — intelligent agents that can act autonomously across systems,” Datta explains.
These agents can streamline complex processes by leveraging tools that facilitate workflow automation and system integration, transforming both risk management and the customer experience. This allows for frameworks like those offered by LangChain and n8n to orchestrate complex operations, such as gathering Open Banking data and assessing credit risk in minutes.
Crafting intelligence with prompt engineering
Prompt engineering represents a fundamental change in developing financial technology, shifting the focus from months of hardcoding logic to crafting sophisticated behaviors with natural language. This approach is particularly transformative for FinTech, where complex requirements and evolving regulations demand agility. It enables AI systems to conduct nuanced financial conversations, interpret regulatory language, and process documents with greater depth.
“Prompt engineering represents a paradigm shift in how we build financial technology solutions,” states Datta. “Instead of spending months hardcoding logic, we can now craft sophisticated behaviours through precisely designed natural language instructions.”
This method allows for dynamic customer interactions, such as explaining loan terms differently based on a borrower’s financial literacy. This approach aligns with new paradigms in Explainable AI (XAI) that utilize natural language and reasoning traces.
Moreover, prompt engineering helps embed fair lending practices directly into an AI’s reasoning process. “The future I envision is one where prompt libraries act as living repositories of institutional intelligence—capturing best practices in credit evaluation, risk management, and customer service,” Datta adds. These evolving libraries can adapt to new markets and regulatory conditions, making tools like LangGraph essential for implementing structured, auditable AI workflows.
Improving accuracy and fairness
In credit risk modeling, AI is enabling a transition from static scoring to dynamic, real-time assessments. By analyzing live financial behavior and alternative data, these systems can make faster and more nuanced decisions. This capability presents an opportunity to create more inclusive lending systems, but it also underscores the critical need to ensure fairness and avoid perpetuating historical biases.
“Traditional lending has long relied on rigid rules and limited data, often missing the nuance in how people actually manage their finances. AI breaks through those barriers — analysing alternative data, understanding context, and making faster, fairer decisions in real time,” Datta observes. While some research suggests traditional models like Logistic Regression can still outperform generative AI in predictive accuracy, other studies show that Generative AI can lead to lower overall bias in credit assessments.
The goal is to build systems that are both intelligent and equitable. “The real opportunity lies in creating lending systems that learn, adapt, and explain themselves — where customers understand decisions and feel supported, not judged,” she continues.
Advanced AI-powered models using ensemble methods claim to achieve significantly higher prediction accuracy and reduce default rates, demonstrating the potential of these new technologies to refine risk modeling when implemented responsibly. This is supported by claims from platforms like Lendro.AI, which reports prediction accuracy of up to 96% with its models.
Defining responsible AI in finance
As AI becomes more integrated into financial services, establishing principles for its responsible use is a major priority. Responsible AI is defined by its commitment to transparency, privacy, fairness, and security, ensuring that automated systems serve customers’ best interests while remaining compliant and commercially viable. Every decision affecting a customer must be explainable in plain language.
“Responsible AI, to me, means building systems that genuinely serve customers — operating transparently, protecting privacy, and treating everyone fairly — while staying secure, compliant, and commercially sustainable,” Datta defines. This approach requires that fairness is an integral part of the system’s design, not an afterthought. Regulatory frameworks like the EU AI Act categorize AI in credit scoring as high-risk, mandating stringent compliance.
Human oversight remains a cornerstone of this framework. “Fairness isn’t something to test after the fact; it must be built in from the start,” she insists.
Even with advanced AI, complex or borderline cases should involve human review to prevent automation bias. As stipulated by Article 14 of the EU AI Act, high-risk systems must be designed to allow human intervention and oversight.
The challenge of behavioral data
The use of behavioral data in digital lending highlights the delicate balance between innovation and responsibility. AI algorithms can analyze how an applicant interacts with a loan application to make lending decisions in minutes, providing rapid access to funds. This innovation offers clear benefits but also carries significant risks, such as the potential for unintentional discrimination.
Datta cautions that, “Behavioural signals could unintentionally disadvantage people with disabilities or reflect socioeconomic bias.” This kind of issue, often termed emergent bias, can arise when historical data no longer reflects current societal realities. It is therefore crucial to ensure that detected patterns are genuinely predictive and not just statistical noise.
To mitigate these risks, a balanced approach is necessary. “The responsible way forward is balance — validating that behavioural insights truly add value, testing for bias, being transparent about data use, and keeping humans in the loop for complex cases,” she explains. Techniques such as adversarial debiasing, which involves training a model against a network that actively seeks bias, can help ensure fairness is maintained alongside performance.
Era of personalized finance
Combining Open Banking with AI has the potential to usher in an era of hyper-personalized and compliant financial experiences. With customer consent, AI can analyze live transaction data to make faster, fairer lending decisions and offer proactive financial guidance. This synergy allows for the creation of services that are not only efficient but also deeply attuned to individual customer needs.
“I’m also building autonomous AI agents that securely connect to multiple banks (with consent), aggregate and analyse data, flag risks, and create complete financial profiles within minutes,” Datta says. This makes credit assessments quicker and fully auditable for compliance. Such processes must adhere to a comprehensive risk management process covering the entire AI lifecycle to manage privacy risks associated with sensitive user data.
Trust is the key to making this model work. Customers must have clarity on how their data is used and how it benefits them. “Done right, Open Banking and AI make finance not just faster — but fairer, more personal, and genuinely human-centred,” she concludes. The ability to audit these complex systems is critical, with frameworks proposing seven distinct levels of access for algorithm auditing to balance transparency with proprietary protection.
Extending automation with intelligence
AI is not replacing existing automation and cloud engineering achievements; rather, it is extending them by adding layers of intelligence and adaptability. The shift is from traditional, rule-based automation to intelligent systems that understand context, learn from outcomes, and improve decisions over time. This evolution is particularly evident in the rise of agentic AI in lending workflows.
“AI doesn’t replace those achievements — it extends them, adding intelligence to automation and scale to decision-making,” Datta clarifies. She develops systems where autonomous agents manage loan processing, fraud detection, and compliance auditing with minimal manual input. This represents a hybrid architecture balancing predictable workflows with deliberative agents for complex tasks.
These sophisticated systems rely on the scalability and security of the cloud to process massive datasets and serve real-time predictions. “These agents don’t just automate tasks — they make contextual decisions, self-improve, and handle exceptions intelligently,” she adds. Deploying these systems effectively requires specialized observability platforms to monitor behavior and manage costs, with tools like Langfuse offering production-ready solutions for tracing and analytics.
Building a human-centered ecosystem
Looking ahead, the goal is to shape a FinTech ecosystem where technology, business, and humanity are seamlessly integrated. The vision is for AI to amplify human opportunity and create intelligent, ethical, and inclusive financial solutions. This requires leaders who can bridge the gap between advanced technology and real-world business and customer needs.
As a Product and Technology SME, “I see my role as a bridge — connecting data, AI, and automation with business strategy and customer outcomes,” Datta states. Her focus is on building transparent and sustainable systems where innovation aligns with trust and human value. This vision is supported by a proposed lifecycle governance model for AI in FinTech that integrates accountability, ethics, and regulatory adaptiveness.
The objective is to create financial technologies that not only drive growth but also strengthen societal trust and equality. “The future I’m working toward is one where people enjoy what they build — where AI reduces stress, removes barriers, and frees us to think beyond today’s limitations,” she concludes. Advanced methods like causal inference for debiasing alternative data are part of this forward-looking toolkit, aiming to build a more equitable financial future.
The rapid integration of AI into lending is poised to redefine the industry, offering unprecedented opportunities for efficiency and inclusion. However, the true measure of its success will be the ability of innovators and institutions to build these powerful systems on a foundation of responsibility, fairness, and a steadfast commitment to human-centered design. It is this balance that will build the trust necessary for a smarter, more equitable financial world.
