The bank of the future: How to embrace AI and analytics
Banks must invest in transforming capabilities across all four layers of the integrated capability stack, says McKinsey & Co.
In order for banks to successfully navigate the digital age, they must be able to establish an artificial intelligence (AI) and analytics capability stack that will help them extend real-time personalised solutions to their customers, McKinsey wrote in its research compendium titled “Building the AI Bank of the Future.”
Speaking to the Asian Banking and Finance, McKinsey partners Renny Thomas and Violet Chung discussed more the report and offered suggestions on what banks should consider before dipping their toes into advanced analytics and machine learning. They believe that lenders will need to move towards an enterprise-wide road map for deploying Advanced analytics (AA) and machine learning (ML) models that would also include plans to embed AI in business processes.
Moreover, banks should also be able to build infrastructure and processes such as data measurement and performance reviews in order to encourage continuous improvement beyond the first model deployment, Thomas and Chung said.
What should banks keep in mind first before jumping into AA and machine learning? Is the organization ready?
AA and ML are capabilities, but banks need the strategy, vision, governance, infrastructure, culture, and talent to be ready as well. To establish a robust AI-powered decision layer, banks will need to shift from attempting to develop specific use cases and point solutions to an enterprise-wide road map for deploying AA and ML models across entire business domains.
In addition to strong collaboration between business teams and analytics talent, this requires robust tools for model development, efficient processes (e.g., for re-using code across projects), and diffusion of knowledge (e.g., repositories) across teams. Beyond the at-scale development of decision models across domains, the road map should also include plans to embed AI in business-as-usual processes. Often underestimated, this effort requires rewiring the business processes in which these AA/AI models will be embedded; making AI decisions “explainable” to end-users, and a change-management plan that addresses employee mindset shifts and skills gaps.
To foster continuous improvement beyond the first deployment, banks also need to establish infrastructure (e.g., data measurement) and processes (e.g., periodic reviews of performance, risk management of AI models) for feedback loops to flourish. Additionally, banks will need to augment homegrown AI models and talents.
How has the word “customised” evolved in terms of customer engagement? Up to what point do clients want products to be tailored to them?
Customers expect their bank to be present in their end-user journeys — knowing their context and needs no matter where or how they interact with the bank, creating a frictionless experience. Banks will need to move beyond highly standardised products to create integrated propositions that target “jobs to be done.” This requires embedding personalisation decisions (what to offer, when to offer, which channel to offer) in the core customer journeys and designing value propositions that go beyond the core banking product, including intelligence that automates decisions and activities on behalf of the customer. Furthermore, banks should strive to integrate relevant non-banking products and services that – together with the core banking product – address the customer’s end needs comprehensively.
Another necessary shift is to embed customer journeys seamlessly into partner ecosystems and platforms so that banks can engage with customers at the point of end-use, as well as take advantage of the partners’ data and channel platforms to increase overall engagement and usage.
Lastly, banks will need to redesign overall customer experiences and specific journeys for omnichannel interaction. This involves allowing customers to move across multiple modes (e.g., web, mobile app, branch, call centre, and smart devices) seamlessly within a single journey while retaining and continuously updating the latest context of interaction. Customisation will no longer be just within the product, but throughout the whole experience – which includes channel, product, and value proposition.
How can banks utilise AA/ML to offer clients customised solutions without appearing too intrusive or persistent?
Banks should be using AA/ML across various aspects of the customer life cycle. Having appropriate KPIs for different functions will also help to ensure they are fully optimising customer experience - not being overly persistent or intrusive without meeting their clients’ needs.
How can banks make sure that their AA/ML engines are consistent across the board, particularly regarding rates and fees as well as risk assessment? In your opinion, how often should recalibration and stress tests be performed?
Banks should be making sure that their AA/ML models can be assessed near real-time across the customer life cycle and that these models should be repeatable and scalable. With that, banks can qualify new customers for credit services, determine loan limits and pricing, and reduce the risk of fraud.
Should banks worry about the possibility that regulators might eventually limit the kind of data that the sector can access? How about their partnerships with third-party providers, especially with issues around security and privacy?
As banks design and build their centralised data-management infrastructure, they should develop additional controls and monitoring tools to ensure data security, privacy, and regulatory compliance. As an example, this could look like timely and role-appropriate access across the organisation for various use cases.
Furthermore, while accessing and leveraging the personal data of customers, banks must secure data and protect customer privacy in accordance with local regulations (e.g., the General Data Protection Regulation in the EU and the California Consumer Privacy Act in the US). Without a centralised data backbone, it is practically impossible to analyse the relevant data and generate an intelligent recommendation at the right moment. If data constitute the bank’s fundamental raw material, the data must be governed and made available in a secure manner that enables analysis of data from internal and external sources at scale for millions of customers, done in (near) real-time and at the “point of decision” across the organisation.
Lastly, for various analytics and advanced-AI models to scale, organisations need a robust set of tools and standardised processes to build, test, deploy, and monitor models, in a repeatable and “industrial” way.
What does the future look like in terms of AI banking?
To become AI-first, banks must invest in transforming capabilities across all four layers of the integrated capability stack: the engagement layer, the AI-powered decisioning layer, the core technology and data layer, and the operating model. This was referenced in Exhibit 6 of the article “AI-bank of the future: Can banks meet the AI challenge?”
The first layer is about reimagining the customer engagement layer. Increasingly, customers expect their bank to be present in their end-user journeys, know their context and needs no matter where they interact with the bank, and to enable a frictionless experience.
The second layer is about building the AI-powered decision-making layer. Delivering personalised messages and decisions to millions of users and thousands of employees, in (near) real-time across the full spectrum of engagement channels will require the bank to develop an at-scale AI-powered decision-making layer.
Layer three is strengthening the core technology and data infrastructure: Deploying AI capabilities across the organisation requires a scalable, resilient, and adaptable set of core-technology components. A weak core-technology backbone, starved of the investments needed for modernisation, can dramatically reduce the effectiveness of the decision-making and engagement layers.
Lastly, the fourth layer is transitioning to the platform operating model: The AI-first bank of the future will need a new operating model for the organisation to achieve the requisite agility and speed needed to unleash value across the other layers. While most banks are transitioning their technology platforms and assets to become more modular and flexible, working teams within the bank continue to operate in functional silos under suboptimal collaboration models, which often lack alignment in terms of goals and priorities.