Unleashing the Power of Groq: A Revolution in AI Performance
Unsupervised learning, a cornerstone of artificial intelligence, has emerged as a powerful technique that enables machines to uncover patterns and insights from unlabelled data.
Artificial Intelligence is an integral tool in the banking and finance industry and reshaping the way the institution is improving customer service, fraud detection, and lending services. The technology is making banking processes not only faster but safer and operations more efficient.
According to BuiltIn, AI in banking industry is expected to keep growing and it is projected to reach $64.03 billion by 2030. From a recent Deloitte survey of IT and line-of-business executives, 86% of financial services AI adopters say that AI will be very or critically important to their business’s success in the next two years.
The Rise of FinTech
Bank use Fintech or Financial Technology for back-end-processes and monitoring of accounts and activities “behind-the-scene” of consumer-facing solutions like your bank’s mobile app or website to check your account balances, deposit checks or transfer funds. Individuals use fintech to access various bank services including payment non-contact payment options through smartphones.
The rise of FinTech has proven the unbounded potential of AI and ML application in banking and finance. This can be through AI-powered real-time biometrics and facial recognition when opening accounts remotely using bank apps. The platform is used to scan identification documents to verify identity and also use the same information when verifying transactions.
Use Cases of AI in Banking and Finance
AI Enabled Future: Integrating AI in Banking
Develop an AI Strategy
The enterprise must develop and AI strategy aligned with the organisation’s priorities, goals, and values. It’s crucial to identify the gaps and pain points which AI can help address and problems it can help resolve. This can be in the area of people and processes. Financial institutions must always keep in mind that their AI strategies must always comply with industry regulations and standards.
An important part of AI strategy development is the formulation of policies and internal processes related to data, infrastructure and talent to provide clear guidelines in the organisation’s AI adoption.
Identify and Define Use Case-driven processes
For AI in banking to be effective, it must address the highest-value opportunities for the enterprise. Banks must evaluate how AI can provide a solution to their current operational processes.
After identifying potential ML and AI use cases, proof of concept and feasibility must be looked into.
Drive Sustainable Outcomes
AI and ML enables financial institution to scale up as needed to address the changing landscape of their requirements from data sources, amount of data, and continue to develop to provide solution to new challenges. Making the technology relevant and instrumental in driving innovation and efficiency.
AI Consulting Group can help banks and financial enterprises who wish to start or develop their AI journey.
Speak to our management consultant to explore the best use cases to help your organization solve your most pressing challenges and achieve quick wins.
