September 19, 2025: As artificial intelligence becomes a central tool in financial crime prevention, institutions are confronting an industry wide dilemma: how to harness AI’s power for fraud detection without undermining user privacy or ethical standards. Recent industry reports show that while nearly all financial organizations deploy AI to enhance fraud monitoring, a large majority express serious concerns about data security, bias, and regulatory compliance as risks rise alongside technological adoption.
Financial systems also face evolving threats from sophisticated fraud schemes, increasingly fuelled by generative AI that can create hyper realistic impersonations and synthetic identities, making effective and ethical detection more imperative than ever.
Against this backdrop, academic and practical frameworks for responsible AI are gaining traction as critical tools for institutions aiming to stay ahead of both fraud and compliance risks.
Bridging Ethical AI and Fraud Detection: New Research from Anup Kagalkar
Technical Product Expert Anup Kagalkar presented an important contribution to this conversation at the International Conference of Sustainability, Innovation and Technology held at Symbiosis University. His paper examines the use of ethical artificial intelligence in financial systems, specifically how AI can effectively detect fraud while safeguarding user privacy.
The research acknowledges a central tension in financial AI applications: high performance fraud detection often depends on access to large volumes of sensitive customer data, yet privacy regulations around the world (from GDPR to emerging national frameworks) are tightening how such data may be collected and processed.
Kagalkar’s framework proposes a balanced approach that integrates robust fraud analytics with privacy preserving techniques and governance principles. He emphasizes that this kind of approach is essential as financial institutions adapt legacy systems to modern, AI enabled architectures without sacrificing compliance or trust.
“Our goal with this research was to show that ethical AI in fraud detection isn’t a futuristic ideal, it’s a practical necessity for today’s financial institutions,” said Kagalkar. “Protecting users means not only stopping fraud but doing so in ways that respect privacy, transparency, and fairness.”
Expertise Built on Real World Impact
Anup Kagalkar brings to this issue deep experience in enterprise software engineering and applied artificial intelligence, especially in high stakes environments where accuracy, compliance, and reliability are essential. His work focuses on modernizing large scale mission critical systems, including public sector and pension systems, through AI driven, data centric solutions that improve operational efficiency while maintaining regulatory alignment.
In his current role, Kagalkar collaborates with cross functional teams to modernize legacy platforms, enhance member experiences, and translate complex business and policy requirements into scalable technical solutions. His contributions include leading AI enabled automation programs, enterprise system refactoring, and data modernization initiatives that have demonstrated measurable improvements in processing accuracy and cycle times.
He is also a published researcher and co author of peer reviewed work on AI driven automation, and has been invited to speak at professional conferences on the responsible adoption of AI for transforming legacy systems.
Industry Insight: Why Responsible AI Matters Now
The financial industry’s increasing reliance on AI for fraud detection comes with significant challenges. Recent studies show that fraudsters are using AI themselves to craft more sophisticated attacks, including deepfakes and synthetic identities, forcing institutions to evolve defenses quickly while navigating privacy expectations and compliance frameworks.
At the same time, financial organizations report that privacy preserving tools and user controlled privacy preferences can reduce detection accuracy, creating a complex trade off between innovation and ethical obligations.
Research and practical frameworks, such as those presented by Anup Kagalkar at the International Conference of Sustainability, Innovation and Technology, highlight that responsible AI adoption, including explainable models, modular system design, and strong AI governance, is increasingly critical for financial institutions. Investing in these approaches can help organizations enhance fraud detection while maintaining trust, privacy, and regulatory compliance.
