08.06.2018

3 Typical Use Cases for Decision Management in the Financial Industry

Share article:

Decision Management is increasingly becoming the method of choice for implementing digital transformation strategies in the financial industry. The platform approach of a Decision Management Suite offers a particularly great advantage: Based on one platform, companies can realize any number of tailor-made business solutions. We introduce three typical use cases for decision management.

Customer Acquisition & Customer Retention

Personalized services, seamless customer journeys and the real-time availability of offers are becoming more and more important for success in customer acquisition and customer retention.

In the analogue world, personal contact at the bank branch was sufficient to discuss the individual needs of the customer and to provide a solution through appropriate offers. Going to the bank branch was often indispensable, for example to update the home address after a move. The bank adviser used this opportunity to learn more about the customer’s situation through personal contact, and, as a result, was able to propose a loan, for example, to support the refurbishment of the apartment. In addition, helpful services or improvements for the customer could be discussed directly and the next steps could be deduced.

In the digital world, however, customers use self-services in their online banking portal. Digital interactions replace personal face-to-face contact, and those digital interactions leave a wealth of data. Decision management starts at this point and provides companies with the best approaches to make better operational decisions based on customer behavior and data directly at the digital touch points: Which product is currently most relevant for the customer? What kind of service could the customer need if his or her account is overdrawn? Which loan conditions are valid and how can the application process be processed as quickly as possible (automated) and in a convenient way (digitalized)?

Digital customer acquisition and retention therefore focus on intelligent, operational decisions in order to create added value for the customer.

Typical Objectives:

  • Greater relevance of interactions at all digital customer touchpoints
  • Lower costs and higher efficiency of customer-facing interactions
  • Short time to market for new ideas and changes to them

Use Cases:

 

Risk Management & Compliance

Even in the digital world, companies must meet risk management and compliance requirements. The financial and insurance industries are particularly affected by this: credit risk assessment, fraud detection, anti-money laundering and digital handling of insurance claims are just a small selection of typical decision management use cases.

How do decision management solutions help meet compliance and risk management requirements?

  1. Financial transactions need to be automated and digital. Digital business transformation requires that the accompanying business and regulatory risks are assessed automatically and digitally. Decision management provides the methods and technologies to comprehensively digitalize the corresponding checks and calculations, to store them centrally and execute them consistently and automatically in all systems and business processes.
  2. Financial transactions need to be audit-proof. Transparency and traceability are central aspects of decision management. Which decision model was used to audit a financial transaction regarding sanctions lists? How did the institute ensure that the regulations for investor protection were taken into account?
  3. The involvement of the business and compliance experts is essential. The decision management approach involves the various experts (such as compliance officers or risk analysts) directly in the development and maintenance process of “their” business logic. Decision management relies on a graphical modeling approach to define policies, guidelines, calculations, checks, etc. Decision management thus ensures high transparency and a better business IT alignment. The resulting compliance and risk models are automated and can be integrated into the IT environment easily and consistently.

Typical Objectives:

  • Higher efficiency through automated compliance checks and risk assessment
  • Lower costs and efforts for compliance
  • Audit-proof traceability of operational business decisions

Use Cases:

Decision management expert James Taylor shares his knowledge on how the DMN decision modeling standard helps enforce compliance and risk management needs.

Business Processes & Back Office

Business processes and back-office tasks still lag behind in terms of end-to-end automation. Wherever a certain degree of intelligence (or complex expertise) is required in the process flow, traditional business process management approaches often fail, resulting in interrupted processes. Examples include assigning tasks to the right person (“mailbox routing”), validations and calculations, document classifications, or customer segmentations. Decision management solutions are able to combine different methods and technologies to embed the necessary intelligence into digital business processes.

The two most important approaches to decision management are business rules management and machine learning. In business rules management, subject matter experts define the decision logic as rules (in the form of decision tables or decision trees). Machine learning uses algorithms that autonomously generate insights based on experience (data), which in turn can be embedded in software applications in the form of machine learning models. Increasingly, the two approaches – business rules management and machine learning – are being combined to make even better operational decisions (you can find out more about intelligent automation here).

Decision management focuses on the business logic, which changes more frequently and must be quickly adaptable. The classic business process management, on the other hand, focuses on the workflow logic, which is more stable over time.

 

Typical Objectives:

  • Greater efficiency through automation of operational decision making
  • Intelligent business processes automation
  • Higher business agility and flexibity

Use Cases: