Infosys Senior Vice President Balakrishna DR (Bali) outlines his predictions for how AI will change the world's financial sector.
Artificial intelligence (AI) solutions will enable financial
institutions and banks to optimize their service offerings in this
ever-changing and unpredictable world, giving them a distinct advantage over
their competitors.
AI is currently in an experimental phase and is being implemented in real use cases. Banks use AI bots to engage with customers and perform an automated risk analysis of borrowers.
Identify process
inefficiencies using computer vision, pattern matching, and deep learning.
AI-based anti-money laundering solutions can help prevent fraud, among several
other use cases.
Banks and financial institutions are combining AI with other emerging technologies to drive transformative transformation.
For example,
Infosys is helping Australian banks use data analytics, blockchain, IoT and AI
models to deliver highly accurate retail demand, consumption, and analytics,
displayed via intuitive and easy-to-read dashboards. Helped predict prices and
streamline commerce and retail. Operational Procurement Process.
KI Limitations
The KI is not without limitations. AI, however powerful and efficient, is ultimately a reflection of its creator. It inherits our prejudices that prevent and prevent it from reaching its full potential. Even the most sophisticated AI can falsely reject an application if the borrower is from a particular race, community, or immigrant family.
It is therefore important to build trust elements within AI models by ensuring that the data used is rich, diverse and frequently updated.
Using additional data from non-traditional sources such as social media,
building algorithms that are unaware of traits such as gender, and checking
bias against the same traits are necessary but difficult. Techniques such as
Explainable AI and Ethical AI bring transparency into how AI makes decisions
and update AI models to remove such biases, making them more trustworthy, safe,
and empathetic. can do.
AI Trends at BFSI
As digital transactions, app usage, payment types, and transaction volumes increase, AI will play a key role in improving customer service and increasing the security of customer assets.
A good example is his
Daniel chatbot at UBS which answers investor questions about market trends.
Similarly, risk mitigation is an area where AI should be active. For example, a large European bank successfully implemented his AML and KYC analysis into their customer onboarding process.
The company has achieved
nearly 50% automation as AI models help segment entities, form cluster groups,
and apply Suspicious Activity Reporting (SAR) rules.
In an era where personalization is key to retaining customers and increasing sales, AI can expand the use of data to create highly personalized services. For example, DBS updated its mobile app offering to leverage AI models to deliver over 100 automated, personalized insights to end customers.
Robo Advisors can be used in various banking functions to
increase sales. For example, recommending investment products, providing nudges
to users, sending investment alerts,