Put AI for Decision-Making into Practice - Decision Intelligence

Businesses are looking to get a higher return out of artificial intelligence (AI) and machine learning (ML) than just great insights. They need access to recommendations that help simplify complex decisions around how scarce resources should be allocated, how to schedule tasks, and how to deal with constraints

Decision Intelligence is an attempt to bring together everything in the field of AI and other engineering fields related to decision-making in a uniform manner

Decision Intelligence (DI) can bridge the gap between guessing at customers’ wants and needs and knowing specifically how to put plans into action. This knowledge and confidence starts with trusted data, which empowers banks to make effective decisions to meet market challenges.

As explained by Thought Leader Arash Aghlara Why Decision Intelligence is important

All algorithms, components, techniques, and methods in Decision Intelligence come from different practices such as statistics, computer science, data science, operational research, and so on.

Decision Intelligence (DI) is a convergence of all relevant techniques in each of these fields, bringing them together with the intent of creating a discipline to help organizations understand and reengineer how decisions are made and how their outcomes are evaluated.

By using a Decision Intelligence Platform (DIP) organizations can break the silos by

??Modeling the business decision cohesively

??Integrate (out-of-the-box) a wide range of technologies such as rules, data, AI/ML, optimization etc., into the decision model

??Orchstrate people, systems, and processes around the business decisions with out-of-the-box orchestration capability

??Deploy as a service and operationalize the business decisions across the enterprise

Why banking and Financial Services need Decision Intelligence

In the personal banking segment of the market, consumers are generally less satisfied with their banking services, and more likely to switch banks, according to Deloitte . On the corporate banking side, customers want real-time analytics, not end-of-day batch processes, reports Finextra . Banks need to generate insights rapidly from their data sources directly while allowing customers to see account status instantly.

Find and grow new customers

• Maximize revenue growth opportunity and share of customer wallets
• Improve speed-to-wallet opportunities to accelerate revenue
• Identify customer profile changes quickly, optimizing service, revenue, and risk
• Automate inefficient manual processes to reduce costs and improve the customer experience

Protect customers and optimize revenues and costs

• Identify additional risks to reduce the cost of financial crime
• Improve speed of identification
• Remove redundant, ineffective, and manual controls
• Accelerate and support digitally driven services and transformation initiatives
• Maintain accurate and perpetual Know Your Customer (KYC) records and reduce the operational cost of manual processes

Unify data from multiple sources

• Create an enriched, accurate, and complete view of customers and suppliers
• De-duplicate and join data even without keys.
• Create reusable data products to support multiple business use cases from the same platform
• Ingest data once and use it for many use cases