Banks have all the data they need to understand their customers, but they often struggle to uncover insights that can forge deeper relationships and drive personalized service. AI in financial services can close the gap.
When it comes to being customer centric, banks have an advantage: a trove of data, everything from transaction histories, product usage, and risk profiles to online searches and campaign responsiveness. But they also have disadvantages: legacy technology, data scattered across the organization, silos instead of synergies. This makes it hard to create a comprehensive customer view—let alone to leverage AI in banking solutions.
SmartBanking AI from BCG addresses these challenges head-on, making it simpler to bring data into a single platform, apply the latest technology for AI in financial services, and reach out to the right customer with the right action, when and where it matters. Integrating advanced analytics into operating models can boost profits by 20% to 30% and pave the way for fully customized experiences and personalized offerings across all client segments.
Financial services is about relationships. Bankers—in particular, relationship managers—continually strive to better understand and engage their customers. Deeper relationships are more effective—and more rewarding, for bank and customer alike. SmartBanking AI accelerates the process.
SmartBanking AI creates visibility by consolidating customer information from different departments and systems (including data on products, transactions, behaviors, and interactions) onto one central platform. Then it leverages that data to do even more:
Through machine-learning algorithms, SmartBanking AI unearths the treasure within your data, delivering qualified leads, attrition alerts, early credit risk warnings, and much more. It helps banks see patterns and respond with foresight and precision. Unique features accelerate the journey:
BCG GAMMA helps organizations unleash the power of signals hidden deep within their data. But algorithms alone aren’t enough. Companies reap the full rewards of AI when they combine analytics with the right processes, ways of working, and change management.
That’s where the breadth of BCG expertise comes in. Our advanced analytics experts work with thought leaders across the full range of functional, technical, and industry areas. This lets us take a holistic approach: providing not just the right capabilities but also the right operating model. When companies have both, they can seize the full potential of AI in banking solutions.
Across regions and markets, we’re helping leading players stay on the cutting edge. Dozens of global banks already rely on SmartBanking AI. Here are some examples:
Predictive modeling and a full 360-degree customer view were two components of a sales process update for the bank’s business and commercial banking clients. Optimized cross- and up-sell leads and retention alerts have led to revenue growth of more than 8% of annual run-rate.
Future bad clients were identified 12-18 months in advance thanks to newly developed predictive models and a full business logic and process redesign. Annual reviews were significantly streamlined as a direct result of this work, a savings of more than 30%.
Predictive models and 360-degree data helped a Canadian bank automate its credit decision making for business banking customers—and achieve more than $25 million in annual savings. New algorithms were supported by new processes, including a steering mechanism to determine when decisions require manual assessment or a light review. Meanwhile, a personalized pricing capability (optimizing pricing based on risk and customer characteristics) sparked a revenue boost of more than $75 million.
An end-to-end integration of data, analytics, content, and orchestration enabled a major European bank to shorten lead-processing times, deliver personalized messaging, and create more than 30 new triggers to identify sales opportunities. The bank expects its AI-empowered omnichannel approach (right customer, right product, right channel, right time) to have a €200 million revenue impact by 2025.
We leveraged 70 billion data points to build a 360-degree view of customers. Machine-learning algorithms then allowed the bank to zero in on potential churners—and take steps to retain them. AI also helped the bank identify cross-sell opportunities. The result: a 14% reduction in churn, a two-times-higher uptake rate, and a four-times-lower opt-out rate from bank communications.
The role of the relationship manager is changing. RMs are harnessing the power of data and advanced analytics to attract more new business and deliver more value to clients.
Leading banks are already organizing solution delivery around customer value streams and taking customer engagement to the next level.
The pandemic has accelerated the inevitable; the AI revolution is overtaking banking as we knew it. Banks that don’t transform stand to lose market share to faster, nimbler tech players.
Despite rapid growth, the route to profitability remains challenging for operators.
Traditionally conservative banks that don’t provide the type of data-driven, innovative services offered by Big Tech will fall short of consumers’ expectations. Worse, fintechs are more than ready to step in and meet those needs—even if their methods are suboptimal.
Thanks to IT, banks can see which banking products their clients have, where their clients get their money, and how they spend it. But banks have yet to leverage that data, especially when working with SMEs and commercial customers.
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