Intimate customer relationships emerge through omnichannel and real-time interactions and the processing of huge amounts of data. New, refined, dramatically simplified, and accurate business processes are enabled through these strategic priorities.
Open banking will require platform-based business processes
Initiatives like open banking and regulation like PSD2 inevitably will change competitive forces for banks. In the UK, regulators force innovation and competition by requiring banks to provide information to third parties who can build business models and services on top of a bank’s data. Banks have to invest in IT and adapt their processes to comply to this reform. At the same time, banks have the opportunity to profit from collaboration with third parties to provide new services based on aggregation of their own and of third-party data.
For example, machine learning and speech recognition can enhance and automate customer service.
Improve customer experience
Customers expect their bank to engage with them on their preferred channel and at their preferred times. Leading banks need to invest and ensure they offer the right products at the right time based on each individual customer’s and household’s needs. Key success factors are:
• Consistent, individualized, channel-independent offerings.
• Improved customer activation and on boarding processes.
• Offering seamless services across channels.
For example, machine learning and speech recognition can enhance and automate customer service.
Reduce operating costs
• Introduce and refine internal, partner, and bundled products at market speed.
• Increase efficiency through simplified and automated processes.
• Enable a consistent and relevant customer experience in real time for both retail and commercial banking.
• Automate back-office processing using rules engines and advanced exception handling tools.
Digital end-to-end processes can be orchestrated and integrated with customer channels and third parties. An open and agile platform and can help banks to reduce time to market and enable personalization as well as standardization of product processes.
For example, machine learning capabilities can help drive efficiency through higher automation achieved with a selflearning payment posting rules engine.
Predict customer needs
By monitoring and measuring social media, transaction history, and external information, digital banks can anticipate customer needs in real time.
Key success factors are:
• Evaluation and analysis of structured and unstructured customer information in real time.
• Anticipating and offering products and services to match customer needs.
For example, Big Data analysis enables optimal, personalized product packaging based on the particular customer profile.
Meet regulatory and compliance standards
The top priorities for finance executives are:
• Support of business strategy execution and decision making
• Cost controlling
• Compliance to banking regulations
• Real-time insights
Maintaining all relevant financial data at the most granular level on one data platform allows banks to quickly react to the latest regulatory reporting requirements. They’re also able to then deliver real-time insights, while minimizing the need for integration and data harmonization.
For example, automated fraud management made smart with machine learning can help banks to comply to ever changing regulation.
