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In a sector where innovation moves faster than regulation, Bidipta Datta exemplifies the new FinTech engineer—one who unites automation, compliance, and customer experience under a single goal: trust. With over a decade of experience streamlining the financial product lifecycle at NewDay through TCS, Datta has built frameworks that transform complex compliance and risk systems into scalable, secure, and business-aligned solutions. Her approach—embedding regulatory checks into automation pipelines and designing data-driven processes that accelerate decision-making—demonstrates how financial technology can move at startup speed without compromising accuracy or integrity.
In the rapidly evolving financial technology landscape, the gap between business strategy, technical execution, and regulatory compliance presents a critical challenge.
As firms race to innovate, professionals who can translate complex financial requirements into robust, secure, and customer-centric technology solutions are becoming essential for sustainable growth. Bidipta Datta, a FinTech specialist with over a decade of experience, has built her career at this crucial intersection.
Working with NewDay through Tata Consultancy Services, she has focused on automation, cloud data engineering, and the end-to-end financial product lifecycle, from customer acquisition and credit risk to collections and Open Banking. Datta’s work reflects a broader industry shift, where technology is not just a support system but a core driver of business value and regulatory adherence.
The transition from a purely technical role to one that bridges business vision is often an evolution shaped by experience. This perspective is important in an industry where the financial consequences of non-compliance can be significant.
Research shows that the average cost of non-compliance is nearly 2.71 times higher than maintaining a strong compliance program. To manage this, fast-growing firms must hire specific compliance roles at appropriate stages.
Datta describes a turning point while working on a credit risk data pipeline that reshaped her approach. “I realized my tests weren’t just technical checks—they were the backbone of timely loan approvals, accurate affordability assessments, and reliable business risk decisions,” she states.
This realization prompted her to position herself as a bridge between business goals and technical implementation, such as when she automated over 10,000 credit risk validations to reduce processing from a week to two hours.
This mindset has become a foundation of her career, focusing on tangible business outcomes rather than just technical output. “For me, the motivation goes beyond writing code or producing tests. It’s about enabling meaningful financial outcomes for customers, empowering business stakeholders to move faster with confidence, and building solutions that fuel sustainable growth,” Datta explains.
Translating complex financial rules into automated technology is a core component of modern FinTech engineering. As institutions digitize services, they must handle vast volumes of data where accuracy and speed are paramount. For instance, advanced machine learning models have shown greater accuracy in credit risk decision-making compared to traditional linear methods.
In her role as a Senior Automation Lead and SME, Datta has led initiatives that address these challenges. “I designed frameworks that could handle 10,000+ validations in just two hours, cutting manual processing time from a week,” she notes. This automation accelerated decision-making for product managers and ensured compliance teams could trust the integrity of the risk data.
Beyond processing speed, ensuring stability across multiple platforms is critical for the customer experience. For sponsor banks offering Banking as a Service (BaaS) platforms, information security and robust testing are often mandatory compliance areas. Datta adds, “By introducing cross-device testing with Appium and BrowserStack, we accelerated mobile application release times by 35% while improving stability.”
A significant hurdle in FinTech projects is the communication gap between product, engineering, and compliance teams, who often have different priorities. This requires a common framework for understanding requirements and outcomes, a challenge that mirrors the need for collaborative AI governance frameworks in the broader tech industry.
Developing clear communication channels is a key part of risk assessment, with some models integrating a Delphi risk communication platform for expert interaction. Datta identifies this as a primary challenge. “One of the biggest challenges I’ve seen between product owners, engineers, and compliance teams is that each group speaks a different ‘language,’” she says.
To overcome this, she focuses on translating technical tasks into shared business outcomes, framing discussions around end-user impact to align teams around a unified goal.
To formalize this communication, Datta advocates for industry-standard practices. “I’ve seen success using BDD (Behaviour Driven Development) and compliance-aligned acceptance criteria as a bridge,” she explains. By expressing requirements in plain language scenarios, the connection between rules and system behavior becomes clear, reducing ambiguity.
In a regulated field like finance, compliance cannot be an afterthought. Regulatory requirements must be integrated into the product design process from the start. This approach is seen in emerging technologies like frameworks designed to automatically generate GDPR-compliant smart contracts or protocols using zero-knowledge proofs to ensure blockchain privacy and compliance.
Datta’s approach involves embedding these rules directly into CI/CD pipelines as quality gates. For Open Banking, she explains, “CI/CD pipelines incorporated contract testing, schema validation, and encryption checks, guaranteeing that every API change adhered to Open Banking specifications.”
For collections and delinquency management, she built workflows that applied fair treatment rules consistently, monitored by dashboards that flagged anomalies early.
This method ensures that compliance is a continuous, automated process rather than a final, manual check. “By embedding compliance into design, I’ve helped deliver faster product launches that are still regulator-ready, ensured trustworthy customer experiences, and created a model where compliance, product, and engineering collaborate rather than conflict,” says Datta.
In high-stakes financial product releases, identifying potential roadblocks is crucial. A dual focus on business priorities and technical details allows leaders to anticipate risks that could disrupt timelines or create regulatory exposure. This requires advanced risk assessment, moving beyond traditional models toward methods like Supervised Dynamic Probabilistic Risk Assessment to manage complexity.
Datta recalls a critical credit product release where speed to market was a key business demand. “I recognised early that even a minor defect in affordability checks or payment workflows could disrupt loan approvals and expose the company to regulatory risk,” she says.
To address this, she championed a test-driven deployment approach, embedding core business and regulatory scenarios into automated pipelines using GitHub Actions, a method validated by studies comparing traditional and simulation-based risk assessments.
Every deployment was validated against these quality gates, catching potential issues proactively. “Product teams gained confidence that time-to-market wouldn’t be compromised, compliance officers saw regulatory checks seamlessly enforced, and engineers had visibility into technical quality,” Datta notes. This alignment prevented silos and demonstrated how technical execution can directly protect business priorities.
As financial services become increasingly technology-driven, professionals require a blend of specific skills. These competencies go beyond technical proficiency to include deep domain knowledge and effective stakeholder communication.
The European Banking Authority (EBA) has noted the growing use of machine learning techniques in risk modeling, while other studies emphasize that successful AI integration is highly dependent on human resource readiness.
According to Datta, three skill areas are essential: domain expertise, technical excellence, and strategic communication. “Deep domain expertise is critical. In my case, this has meant understanding the end-to-end financial lifecycle—credit risk modelling, customer onboarding, open banking, collections, and regulatory compliance,” she states. This context ensures technical solutions address the right business problem.
Technical excellence in automation and data engineering is equally important. Datta emphasizes that, “These skills are what ensure that financial services can scale securely, deliver faster, and maintain accuracy.”
Finally, she points to strategic communication as the bridge that aligns all stakeholders, translating regulatory requirements into clear criteria for engineers and explaining technical trade-offs to product teams.
Adopting new tools is key to innovation, but in FinTech, it must be balanced with compliance and stability. A strategy of structured experimentation allows for progress without disruption. This approach is formalized in many jurisdictions through regulatory sandboxes, which allow firms to test innovations in controlled environments.
Datta’s approach is to pilot new technologies on contained use cases where benefits are clear and risks are measurable. “For example, I first introduced Snowflake + DBT testing pipelines in parallel with existing ETL systems, validating accuracy through automated regression suites before full adoption,” she says. This allowed the team to innovate without interrupting production.
The second principle is compliance-by-design, where any new tool must support auditability and data governance, aligning with frameworks like the EU AI Act’s risk-based framework. “When automating Open Banking consent validations, I embedded compliance rules directly into the CI/CD pipeline, ensuring no release could bypass PSD2 consent flows,” Datta explains. This method strengthens compliance while fostering innovation.
Looking ahead, artificial intelligence is set to further transform the financial industry, which is projected to contribute $13 trillion in economic growth to the world economy by 2030. While AI offers powerful opportunities, it also introduces new risks around data security and algorithmic bias. A responsible, security-first approach is essential, leveraging tools like AI-powered smart contract auditing to ensure code logic and security.
Datta sees her future role in designing FinTech solutions where security and compliance are embedded into AI-driven frameworks. “For me, the future of FinTech testing lies in building AI-powered agents that can simulate complex financial scenarios—credit approvals, fraud checks, delinquency restructuring—while running validations across thousands of edge cases,” she states. This requires a regulator-ready framework from the start.
Her vision involves integrating privacy-by-design practices like tokenization and encryption so AI models can learn safely. “My vision is to make sure innovation and compliance move together: Every AI-driven testing or data framework I build must accelerate delivery while strengthening security and regulatory trust,” Datta concludes. This ensures financial products are not just innovative but also safe and trusted by regulators and customers.
As the FinTech industry continues its rapid growth, the need for professionals who can effectively merge business acumen with technical execution will only increase. By embedding compliance into the core of product design and fostering clear communication across teams, leaders can build a foundation of trust that supports both innovation and stability.
