CIOInsider India Magazine
AI Safeguarding Data and Privacy in Digital Ecosystems
Tapesh Bhatnagar, Head of Digital Solutions at Giesecke+Devrient (G+D), is a sales professional skilled in software and services focused on Digital Payments, Customer Experience, Technology updates, and Operational efficiency.
The swift increase in the speed of AI advancements in recent years heightened interest in AI innovation within finance, partly because of the ease of use and intuitive design of GenAI tools. The application of AI in financial markets with complete end-to-end automation and no human involvement is still primarily in the development stage; however, broader implementation could heighten existing risks in financial markets and introduce new challenges. In an interview with CIO Insider, Tapesh Bhatnagar, Head Digital Solutions, Giesecke+Devrient (G+D) shares his insights on application of AI and GenerativeAI in the current environment and potential risks and unintended consequences of using AI.
How can generative AI be used to provide customized financial advice and product suggestions based on individual customer data and financial goals?
Generative AI is fundamentally transforming how financial institutions deliver personalized advice to their customers. When implemented through chatbots powered by large language models, these systems enable natural, context-rich conversations that leverage comprehensive customer data including spending patterns, savings objectives, and transaction history.
The true strength lies in the system's ability to analyse this data and provide relevant recommendations at precisely the right moment. For instance, if AI identifies that a customer frequently travels and uses their credit card for travel-related expenses, it can intelligently suggest a low-interest travel credit card that aligns with their lifestyle and spending habits.
What sets this technology apart is its real-time product matching capabilities. AI continuously analyses behavioral data to deliver optimal product suggestions when they're most relevant. This might involve recommending a high-yield savings plan when a customer's account balance consistently exceeds a certain threshold or suggesting investment options that align with major life stage goals such as retirement planning or funding a child's education.
The continuous learning aspect is particularly valuable. Each interaction refines the AI's understanding of the customer's evolving financial priorities and risk tolerance, ensuring that recommendations become increasingly sophisticated and personalized over time.
Importantly, maintaining robust data privacy and security protocols is essential when leveraging generative AI for personalized advice, ensuring customer trust and regulatory compliance in all recommendations.
Also Read: India's Ascension as a Digital Payments Leader from a Cash-Only Economy
In what ways can generative AI help detect and prevent fraud by examining large data sets and identifying unusual activity trends?
Artificial intelligence plays an important role in
improving fraud detection and prevention, to some extent in the financial sector. One main use is anomaly detection across different channels, where AI examines large data sets that cover devices, locations, and transaction types to find deviations from a user’s usual behavior. For instance, logging in from an unknown device or showing unusual transaction patterns can trigger alerts automatically for further checks.
Additionally, biometric and behavioral analysis uses subtle cues, such as typing speed and touch pressure, to identify discrepancies that might indicate fraudulent activity even before a transaction is finalized.
Another valuable tool can be real-time risk-based authentication (RBA), where AI assesses risk signals to adjust authentication requirements dynamically. This means multi-factor authentication is activated only when a transaction looks suspicious, improving security without hindering the user experience.
What role does AI play in ensuring data security and privacy in digital ecosystems?
Ensuring security and compliance is essential for the responsible use of AI in financial services. G+D Netcetera is focusing on privacy-by-design principles. This involves methods like tokenization and anonymization, which protect sensitive customer data even before it is used for AI training or analysis.
To further protect data, especially from exposure to public cloud-based LLMs like ChatGPT, G+D Netcetera offers middleware and on-premises AI options, which allow financial institutions to keep all data secure within their own environments. Additionally, output verification mechanisms monitor and validate chatbot responses or AI-generated content before it reaches users, preventing any accidental disclosures of confidential information and maintaining trust.
AI can also support these efforts by enabling compliance features such as consent tracking, access control, and data usage logs, ensuring alignment with strict local and global regulations like RBI, GDPR and PSD2. These strategies help financial institutions tap into the power of AI while upholding high standards of privacy and regulatory compliance.
One significant concern is the risk of AI-generated misinformation, where models produce confident but incorrect outputs
Could you explain how AI enhances regulatory supervision and compliance in digital environments?
A wide range of AI tools is now essential for simplifying regulatory compliance across jurisdictions while significantly reducing the need for manual oversight. One key feature is the use of AI-powered compliance agents embedded in chatbots. These agents are programmed to operate strictly within regulatory boundaries for example, triggering two-factor authentication for high-value transactions, in line with PSD2 (Payment Services Directive) requirements.
Beyond rule-based automation, AI systems enable real-time monitoring by routinely auditing system logs, flagging compliance issues, and generating actionable reports. This allows institutions to address concerns proactively, rather than relying solely on manual, periodic audits.
What are the potential risks and unintended consequences of using AI in heavily regulated environments?
One significant concern is the risk of AI-generated misinformation, where models produce confident but incorrect outputs. In critical financial situations, such mistakes can mislead customers and have serious outcomes.
Another major risk is data leakage, especially when sensitive customer data is input into insecure, cloud-based LLMs. To reduce this risk, G+D Netcetera recommends using local or sandboxed AI setups in regulated environments.
Also Read: AI Appreciation Day: From Innovation to Transformation
Bias and ethical risks also present significant challenges, as AI systems may repeat past discrimination, such as denying credit based on location or demographic factors, if they are not trained responsibly and regularly reviewed.
Additionally, over-automating decision-making processes may limit necessary human oversight, especially in emotionally sensitive scenarios like loan denials, which could negatively impact customer experiences.
Finally, there is a growing concern that the fast pace of AI development is outpacing regulatory updates. Without proactive governance, this gap can expose financial institutions to legal liabilities and reputational damage.
