Riding the AI tide

In today’s era, everyone knows or has heard about Artificial intelligence, aka AI, GenAI. It seems like the entire globe is under its influence, and hardly any sector or domain will be untouchable from this buzzword (really?). Plenty of AI tools are readily available on the internet; just search it using your favourite search engine (I prefer Google), enter keywords or descriptions or instructions about what you are looking for and boom, just like a Harry Porter’s Magic Wand your desired thing, work is almost (there is the reason why I am saying ‘almost’, you can otherwise agree to disagree) ready, isn’t it a magical thing? Indeed, it is fascinating, or they say in most reality shows ‘stupendous.

I agree that GenAI can save a lot of effort and time to create, define, and derive one thing as compared to the human brain, Of course, AI isn’t here to replace our brains, but rather to work alongside them! AI shines in areas that might make your head spin. Imagine analysing mountains of data to find cures for diseases or recognise patterns in the weather that humans might miss. AI can do this tirelessly, without needing coffee breaks, and free from emotions that can cloud judgment. This makes AI a powerful tool for scientific discovery and solving complex problems objectively.

While AI might not be writing the next great novel just yet, its vast knowledge base can be a wellspring of inspiration. AI can analyse countless ideas and data points, helping humans develop groundbreaking solutions and inventions. Think of it as a tireless research assistant, feeding you information and possibilities to spark your creativity. The future holds promise for AI and humans working together to achieve even greater things.

Nowadays, almost every organisation is using some kind of AI; this consists of various AI tools for drafting mail, creating various content, developing code, preparing presentations, processing data, performing analytics, identifying patterns, performing core business functions, and what not, you name and probably there might be an AI tool for it, you might be interested to know that you can find any AI tool using website – There’s An AI For That -theresanaiforit.com (explore it on your own risk!).

AI in BFSI (Banking, Financial Services, and Insurance) is transforming the industry in various ways. Here are some examples:

1. Chatbots and Virtual Assistants:
AI-powered chatbots are being used to provide 24/7 customer support and assist with queries and transactions.
2. Risk Management: AI algorithms detect and prevent fraud and assess credit risks.
3. Personalised Banking: AI is used to offer personalised financial advice, investment recommendations, and tailored services.
4. Process Automation: AI automates repetitive tasks like data entry and compliance checks.
5. Predictive Analytics: AI analyses customer behaviour, preferences, and market trends.
6. Regulatory Compliance: AI is helping with anti-money laundering (AML) and know-your-customer (KYC) regulations.
7. Digital Payments: AI is enhancing security and convenience in digital payments.
8. Credit Scoring: AI is used to improve credit scoring models, considering non- traditional data sources.
9. Insurance Underwriting: AI is streamlining the underwriting process, assessing risks more accurately.
10. Customer Insights: AI provides actionable customer behaviour and preferences insights.

BFSI is establishing as pioneer to widely adopting AI tide, a dire necessity igniting many creative adoption developments of AI ideas and tools, just to mention. These are just a few examples of how AI is used in the BFSI sector. As technology continues to evolve, we can expect even more innovative adoption of AI in the future.

Also Read | AI and Cloud: Driving Digital Transformation in Insurance

Technology is a boon or bane, and AI is no exception to that, it depends on what tool you use and what that tool is using to produce outcomes, getting confused, let’s understand this way,

Learning from a firehose of data: Imagine a massive library filled with books on every topic. That’s kind of like how Gen AI trains. It’s fed huge amounts of data, like text articles, images, or code. This data helps the AI understand patterns and relationships within that information.

Think like a detective, create like an artist: Gen AI is like a detective who studies the clues (data) to understand the bigger picture. It then uses that knowledge to create new things that are similar to what it learned but also original. For instance, if it’s trained on pictures of cats, it can generate new images of cats, maybe even ones in wacky outfits!

Different tools for different tasks: There are various techniques Gen AI uses to create, like:

• Statistical models: These are complex equations that help the AI understand the relationships between different parts of the data. By analysing these connections, the AI can predict what might come next and use that to generate something new.
• Deep learning: This involves intricate networks of algorithms inspired by the human brain. The AI learns to recognise patterns and recreate them novelly by processing data through these networks.

The future of AI collaboration: Gen AI is still under development, but it’s already making waves in various fields. It can help designers create new products, musicians compose unique melodies, and writers overcome writer’s block. The future looks bright for AI as a creative partner, helping us push the boundaries of innovation.
AI, for all its potential, has some shortcomings we need to consider:

• Thinking outside the boX: AI excels at following patterns and completing tasks based on its training data. But it struggles with genuine creativity, the kind that sparks entirely new ideas.
• Understanding nuance: AI can handle information, but it often misses the subtler aspects of human communication, like sarcasm or figurative language. This can lead to misinterpretations and mistakes.
• Bias in the data, bias in the results: AI is only as good as the data it’s trained on. If that data is biased, the AI will reflect that bias in its outputs. This can lead to unfair or discriminatory outcomes.
• The black boX problem: Sometimes, AI makes difficult decisions for humans to understand. This lack of transparency can be concerning, especially when high stakes are involved.
• Job displacement: As AI automates more tasks, some jobs might disappear.
  While new opportunities will likely arise, retraining and supporting those affected will be challenging.

These are just some of the limitations of AI. As we develop and use AI responsibly, we can mitigate these shortcomings and harness the true potential of this powerful technology.

While AI holds immense potential, its development raises concerns across various aspects. Here’s a glimpse into the hazards of AI from different viewpoints:

Information Security:

• Fortress Breached: AI systems themselves can become targets. Hackers could manipulate the data used to train them, essentially creating a Trojan horse that lets them bypass security measures.
• Data Exfiltration: AI algorithms hungry for data could inadvertently become tools for information leaks. Malicious actors might exploit vulnerabilities to steal sensitive information processed by AI.

Data and Intellectual Property:

• Vulnerability to Bias: AI trained on biased data can perpetuate those biases in its outputs, leading to discriminatory outcomes. This can impact areas like loan approvals or job applications.
• IP Theft by AI: AI’s ability to learn and mimic raises concerns about intellectual property theft. AI could replicate creative work or inventions, blurring the lines of ownership.

Code of Conduct and Ethics:

• AI Gone Rogue? Unforeseen consequences or unintended biases in AI decision-making could violate ethical codes and principles. Imagine an AI-
  powered weapon making an autonomous choice with devastating consequences.
• Who’s Responsible? As AI becomes more complex, assigning responsibility for its actions becomes murky. Who is liable if an AI system makes a harmful decision?

Also Read | AI and Machine Learning Redefining Risk Management

Increased Threat Exposure:

• Deeper Fakes, Bigger Problems: AI could create highly realistic deepfakes, manipulating videos or audio to spread misinformation or damage reputations.
• Cybersecurity on Steroids: AI could empower attackers to launch more sophisticated cyberattacks, exploiting vulnerabilities that traditional methods might miss.

Output Sanity and Completeness:

• Garbage In, Garbage Out: The quality of AI outputs hinges on the quality of data it’s trained on. Incomplete or inaccurate data can lead to unreliable or misleading results from AI systems.
• Black Box Blues: Some AI systems are opaque, making understanding how they arrive at their decisions difficult. This lack of transparency raises concerns about the accuracy and fairness of their outputs.

These are just some of the potential hazards associated with AI. By acknowledging these risks and developing responsible AI frameworks, we can mitigate these dangers and ensure AI is a force for good in the world.

As AI continues to evolve, it’s crucial to navigate this powerful tide with both optimism and caution. By harnessing the immense potential of AI for innovation and problem- solving, while acknowledging and mitigating its potential hazards, we can shape a future where AI serves humanity, not the other way around. This requires a collaborative effort between developers, policymakers, and the public to ensure ethical and responsible AI development that benefits all.

PS – Don’t think that I leveraged AI to come up with this, would you?

Views expressed by Sunil Nishankar, CISO, Future Generali India Life Insurance