The Future of Money and Medicine: Will AI Put Your Fears to Rest? We still fear losing our hard-earned money due to banking scams or ATM thefts. The anxiety surrounding misdiagnosis in healthcare is also real, compounded by concerns about AI biases, leaders addressed these issues with modern solutions

The Indian FinTech and HealthTech markets are expected to reach USD 1.5 trillion and USD 21.3 billion, respectively, by 2025. With the rise of Artificial Intelligence (AI) across various industries, from banking and healthcare to travel, there is a promise of hurdle-free solutions. However, as normal people, we still fear losing our hard-earned money due to banking scams or ATM thefts. The anxiety surrounding misdiagnosis in healthcare is also real, compounded by concerns about AI biases. Leaders from the finance and health-tech sectors addressed these issues at the recently concluded Entrepreneur Summit 2024 at Bharat Mandapam.

Layered Approach Against AI Bias

One of the biggest concerns within financial services as AI adoption accelerates is bias—both in terms of compliance and customer engagement. AI has the potential to inadvertently discriminate against specific types of customers based on the data it analyzes. "It's very important to eliminate bias,"Rajesh Mirjankar, Co-Founder, MD and CEO of Kiya.ai said. "When we look at things such as risk and compliance, it's easy to be drawn into conclusions that certain types of customers must be excluded from certain types of transactions."

As a solution, many financial services firms are adopting layered AI approaches. "We've worked across 50 countries, implementing banking solutions by building a layered approach where we focus on data-driven rules governed by compliance and regulatory norms," Mirjankar explains. This ensures that decisions made by AI are not only efficient but also fair and transparent.

Further, Saurav Swaroop, co-founder and CTO of Velocity added, "When it comes to AI in financial services, the quality of the training data is crucial. Financial companies often use their own private data, and how clean and unbiased this data is can greatly affect the results. It's important to make sure we remove any human biases and carefully check all the data we have on the companies we've worked with. This way, we can ensure that the AI model is fair and reliable."

Computer Vision Technology Against Banking & ATM Thefts

With the advent of technology, banking scams have increased; however, the threats around banking infrastructure have not gone away. Whenever we think of keeping our valuables in banks—whether in lockers or vaults—we still fear ATM thefts and banking loots. However, with the help of computer vision technology and AI, the chances of safety increase. "Computer vision with AI solutions can analyze behavior in real time to identify any suspicious activities," Nitin Jain, Co-founder of Assert.ai, explains. The technology utilizes facial recognition to detect individuals who should not be in the vicinity and can even identify potential threats, such as someone carrying a firearm or prohibited equipment.

Health-Tech

Customized Healthcare Treatment

While sharing inputs on the innovative applications of Generative AI in healthcare diagnostic and treatment planning, Nakul Jain, Director of Solutions, Wadhwani Institute for AI emphasised on customized treatment plans. He noted that by considering various factors, such as a patient's lifestyle, existing health conditions, and even genetic information, AI can facilitate tailored precision medicine. "There's an opportunity for large language models to create individualized treatment plans that minimize the risk of adverse drug reactions," he stated. This personalized approach not only enhances patient safety but also improves overall treatment effectiveness.

Jain also shed light on the challenges of report generation in healthcare. "In many parts of the country, reports are still generated manually or in inconsistent formats, making it difficult for healthcare professionals to access and utilize vital information efficiently." He sees significant potential for generative AI to streamline this process. "By leveraging large language models, we can automate report generation, standardizing formats and improving the speed at which healthcare providers receive critical data."

Remote Patient Monitoring

"The biggest issue that we see today is not just about utilizing existing models and scaling on the revenue front, but identifying where the gaps are in the emerging market. When we launched our program about four to five months ago, we discovered that the prevalence rate for heart failure was 18%, significantly higher than the global average of about 5%," Prateek Golecha, Vice President of Digital Health at Tricog Health India, added.

However, the application of smart rule-based suggestions which empowers clinicians to manage patients effectively post-discharge can help in remote patient monitoring, he further added.