A Definitive Guide To Composite AI in Banking

The global artificial intelligence (AI) in Banking is expected to reach USD 130 Billion by 2027, and register a CAGR of 42.9% during the forecast period, according to the latest analysis by Emergen Research.

Early adopters of Artificial Intelligence in Banking have realized that Isolated AI use cases do not create sustainable value for business and they need a comprehensive AI approach to leverage Composite AI embedded with Domain knowledge.

What is Composite AI?

Composite AI is a breakthrough approach combining multiple AI realms to more deeply interpret data and efficiently solve a wider range of business problems. The techniques applied include- knowledge graphs, natural language processing, contextual analysis, machine learning, deep learning, Computer vision, Recommendation systems, and other methods.

Why Composite AI for Banking?

Banking system deployments have created silos of data which ultimately lead to broken customer journeys and poor data quality for machine learning and analysis.

Composite AI with a common knowledge graph ontology of customer attributes, product attributes, and AI models can help overcome the silos with comprehensive intelligence across customer journeys. Read more

A good example is Contextual banking with Machine Reasoning whereby Big Data, recommendation systems, computer vision, and Natural Language processing are used to hyper-personalize customer journeys for existing customers.

Benefits of Composite AI

A. Intelligent interactions

Composite AI enables intelligent customer interactions with proactive service, contextual cross-sell and upsell, and persona-based offers and recommendations.

B. Frictionless customer journey

Composite AI can enhance digital customer journeys across channels e.g. Higher conversion in Digital sales for new to bank prospects by streaming live product recommendations based on dynamic persona driven by location, device, context understanding from clickstream/NLP chat conversations, machine reasoning based dynamic pricing & instant personalized offers, followed by eKYC/onboarding in the channel with face verification & liveness detection for a frictionless journey.

C. Lightning-fast time to market

Composite AI with prebuilt customer journeys brings built-in best practices, workflows, security, governance, and backend adapters for quick launch.

D. Seamless expandability

A single Data and AI layer with a development toolkit and SDK enable the bank to build new use cases easily from scratch or by reusing the pre-built models e.g. extend the propensity model to churn prevention by leveraging the same data and customer attributes.

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