AI in Banking: Personalization Use Cases
Introduction: AI in Banking
Banks have the information necessary to understand individual clients, but they often fail to surface the insights that could lead to more meaningful and personalized engagement. AI in banking and financial services can close the gap.
Banks want to deliver financial services to clients that are hyper-personalized and drive value, but this includes more than just identification of specific clients with model outputs and risk scores. For example:
- Use AI to highlight clients that might be good prospects for different or additional bank products and services by combining contextual information about the client with external data (eg socio-economic data and 3rd-party credit and other data) and model outputs by product type.
- Leverage AI for personalized insights into which clients are at risk (e.g. defaulting on loans, missing payments or making late payments) and then provide personalized insights to bankers and service agents that provide insights that reduce risk and drive better outcomes. Then, go beyond model and data monitoring to business observability: monitoring business performance across numerous campaigns designed to realize the value of AI applications.
At CognitiveScale, we have helped banks and financial services companies optimize customer engagement and improve the delivery of banking services with our Cortex enterprise AI platform and its specific capabilities in AI-powered Personalization, Business Goal Optimized AI, and Trust and Governance.
AI in Banking: Key Areas for Improvement
For global banking, McKinsey estimates that AI technologies could potentially deliver up to $1 trillion of additional value each year. Per McKinsey, many banks, however, have struggled to move from experimentation around select use cases to scaling AI technologies across the organization. Reasons include the lack of a clear strategy for AI, an inflexible and investment-starved technology core, fragmented data assets, and outmoded operating models that hamper collaboration between business and technology teams.1
CognitiveScale AI Use Cases in Banking
At CognitiveScale, we are working to address these challenges specifically - data, infrastructure and scalability, to name a few - and we have the enterprise AI platform, Cortex, and expertise in AI Engineering to help address the limitations that McKinsey is highlighting, including:
- Compose AI applications at scale
- Leverage AI-powered Personalization and Business Goal Optimized AI to improve multiple lines of business
- Sales Acceleration
- Personalized Customer Engagement
- Service Experience
- Enable compliance, audit and risk management teams to monitor AI applications for trust and responsible use, and drive more robust governance programs
CognitiveScale’s Cortex AI platform has enabled banking clients to build applications across several functional areas of the banking businesses: from acquiring new clients to then delivering better customer engagement (e.g. personalized services), to improving and optimizing back office operations and processes like application processing, to serving customers better via the contact center. CognitiveScale’s Cortex AI Platform also includes Trust and Governance capabilities that are specific to banking.
AI-Powered Sales Acceleration
From acquiring new customers to supporting existing clients through changes in their portfolios of banking products and services, there are several Banking Sales Acceleration AI use cases that leverage AI-powered Personalization solutions:
- Personalized Cross-selling & Up-selling Insights: Based on an individual customer profile, provide hyper-personalized insights about product and service options that may be of value. These insights can be surfaced to bankers, agents, brokers, or directly to customers.
- Intelligent Lead Generation: Based on conversion and churn analysis, derive insights that can improve targeted marketing efforts and ad spend, and drive higher quantity and quality leads to banking and lending websites.
- Personalized Shopping Experience & Offer Presentment: For website visitors and leads that have responded to more targeted ads (based on insights from churn and conversion analysis), personalize the shopping experience as quickly as possible.
AI-Powered Customer Engagement in Banking
AI-powered Personalization solutions can generate hyper-personalized insights by customer and provide much more of a “guided path” banking experience, giving both customers and their bankers insights and interventions that improve service quality, costs, and satisfaction. Banks, lenders, and financial services companies are leveraging AI extensively to improve customer engagement for those that could benefit from changes in their portfolios or those at risk of impact on their financial performance. At CognitiveScale, our Cortex AI platform is helping with customer engagement in a couple of unique ways:
- Goal Optimized AI Campaigns not only help to improve the identification of at-risk customers (e.g. loan default or payment delay) with model outputs (risk scores, predictions, etc,), but our platform capabilities enable organizations to develop goal-oriented campaigns that change customer experience with Mission Plans and interventions that drive value.
- From Model and Data Observability to Business Observability: With personalized customer engagement, it is important to monitor model and application performance, but to realize the full potential of AI, and understand how solutions are impacting business value, solutions need to track KPIs with business observability. The Cortex AI Platform enables this level of AI solution development, operationalization, monitoring and maintenance, specifically tracking the impact on business performance metrics.
Service Experience in Banking
Trends in Digital Transformation have banks (and most organizations) looking to modernize their Contact Centers well beyond the more traditional capabilities of a Call Center, including the use of customer apps and portals as well as chatbots and personal messages. Banks have led the way in digital transformation initiatives with online banking, payment processing and app usage key consumer-oriented use cases.
But banks still suffer from customer challenges like high call volumes, handling times and costs, so there is a desire to build out more personalized, predictive, and proactive service experience capabilities that drive value (cost savings, satisfaction scores, and more).
Banking AI use cases in Customer Experience include:
- Agent Assist Insights: Predicting when a customer will call for specific reasons (product inquiries, payment or billing challenges) along with specific information designed to improve call handling time (AHT) and first call resolution (FCR) - integrated into CRMs for use by call center agents - can make a huge impact on costs and satisfaction.
- Intelligent Call Routing: The same predictions and prescriptions that drive Agent Assist insights can then be leveraged via the CognitiveScale Cortex AI platform to inform IVR systems of the likely reason a customer is calling, thereby impacting the call routing process more intelligently.
- Self Service Channels (e.g. Chatbots): Similar hyper-personalized customer insights can be surfaced in self service applications like chatbots.
- Personalized Messages & Notifications: With personalized insights, clients are trying to drive more digital touches, thereby lowering costs due to call deflection. This not only requires integration of insights into apps and portals, but also business observability of various campaigns (e.g. a campaign to get more clients to use the app, or a campaign to get more app users to respond to requests via the app).
AI Trust & Governance in Banking
Models and decisioning systems have to be trusted, used responsibly, and be compliant with internal and external policies. CognitiveScale’s Cortex AI platform has built in capabilities to help clients manage AI Trust & Governance to meet these needs.
In banking, Trust & Governance use cases can include: lending and credit inclusion and advocacy (or put another way, testing for discrimiation in lending and credit). As banking organizations use models and algorithms to determine who should get credit or loans, there is the need to inspect models and data continuously for bias, explainability, robustness and more - and then companies have to meet the needs of Compliance Officers, Risk Managers, Auditors and Regulators per their governance requirements.
CognitiveScale’s Cortex AI Platform Trust & Governance Capabilities
CognitiveScale Banking Expertise
CognitiveScale has worked with a number of the largest banks in the world, including Wells Fargo, Morgan Stanley, Barclays, HSBC, and J. P. Morgan.
Contact us to explore how we can help your organization drive more value from your Banking AI initiatives.