What makes you choose to buy one product over another? There are many factors that drive a purchase decision, but it is safe to say that price is a key one. For banks, the right pricing strategy has always been a critical driver of profitability, customer acquisition and retention, operating margins, and growth. But in the modern hypercompetitive and dynamic market, traditional pricing strategies no longer work. Static models based on historical data, or broad-based market intelligence, or even the insights of experienced pricing managers no longer suffice to meet customer expectations, competitive pressures, or economic challenges.
Why Traditional Pricing Strategies Are No Longer Enough
Traditional pricing strategies worked on static models. These relied on cost plus approaches, competitor benchmarking, regression analysis and cluster analysis, or demand forecasting. They also depended on the experience of product and pricing managers. And these strategies worked perfectly well until recently. Today, banks are operating in a highly disruptive market landscape. Customers are increasingly demanding personalized, relationship-based, and contextual price offerings that reflect market conditions. And they are not afraid to switch loyalties if their needs are not met. Competition has not only increased but changed – banks no longer compete with other banks for share of wallet. Instead, they must compete with digital native fintechs, tech giants, and neo banks who operate with innovative, agile business models and offerings. Evolving supply chain dynamics, geopolitical tensions, regulatory scrutiny, and global economic instability add to the pressures on banks, making it even more important to get pricing right. Meeting these expectations manually or with rigid models is virtually impossible.
How AI and Machine Learning Can Transform Pricing
The emergence of AI is proving to be a game changer for the banking sector. 51 percent of banks say that AI is changing business at a fundamental level, 80 percent believe that banks that embrace AI will develop a competitive edge over those who do not, and 70 percent plan to increase the percentage of global budget spent on AI.1 Currently, most banks are looking at leveraging AI to transform customer-facing functions and improve productivity. But AI holds significant potential for modernizing pricing as well.
- Real-Time Data Processing: AI’s strength lies in processing vast volumes of data at lightening speeds. AI platforms can analyze customers’ transactions and account history, and map it against market trends, competitor pricing, and macroeconomic indicators to provide intelligent insights in real time for creating personalized pricing offers that are in sync with the market trends.
- Predictive Demand Forecasting: Banks working with traditional pricing methodologies cannot respond quickly to sudden market changes. AI offers predictive analytics capabilities to forecast demand by identifying patterns in customer behavior and market trends like economic shifts, geopolitical issues, or weather events. Banks can leverage this capability to proactively optimize pricing offers.
- Price Elasticity Modeling: Not everyone responds the same way to price changes. And understanding how different customers may react can help banks roll out optimized pricing strategies to maximize profits without impacting loyalty or satisfaction. AI-powered models can continuously learn from customer behavior to model price elasticity at a granular level—by product, geography, customer segment, and even time of day.
- Personalization: Customers expect hyper-personalized price offerings from their banks. And AI can process data pertaining to customer behavior, transaction history, ongoing engagements with the bank, and account balance to offer personalized pricing, offers, bundles and even advisory services.
Benefits and Challenges of AI-Powered Pricing
The shift to AI-powered pricing models can ensure customer delight with hyper-personalized and relationship-based offerings. This is invaluable for shaping positive customer value perceptions and boosting loyalty and trust. Banks can also strengthen their competitive edge by responding faster to changes in the macro environment. AI allows companies to manage complex pricing strategies across thousands of products, markets, and customer segments with minimal incremental cost. It can also reduce human error in pricing processes by eliminating manual oversight or outdated assumptions. Ultimately, an AI-powered pricing strategy can drive high revenue and profit margins.
But technology is not without some challenges and some caution is advisable when using it. AI platforms process vast volumes of data, and in this context, a large percentage of this is customer data. Most customers now understand that their data holds the key to the personalized services they want and are not hesitant to have banks access or use it. But they also expect the bank to safeguard their information. There is also increasing regulatory scrutiny and action on using AI ranging from disclosure and consent requirements to security and ethical usage rules. Banks must step up their cyber security, data privacy, and regulatory compliance efforts to prevent any breaches.
To deploy and fully leverage the power of AI, banks need to separate pricing from the system of records or their legacy core systems. Modernizing these core systems is not really feasible as it is too risky, expensive, and time consuming. Instead, banks can choose to deploy a powerful and intelligent middleware platform that can sit over the legacy core, separate the system of engagement from the system of records, and power AI strategies.
AI is not the future of banking anymore; it is a reality and is already transforming how banks compete and grow. As banks try to meet evolving customer requirements and protect profits in a challenging market, AI can help modernize pricing strategies to give them a distinct competitive advantage.
