In a post COVID reality, the banking sector will continue to grapple with a number of significant challenges ranging from dipping interest rates to an increasingly competitive market and loan defaults. Getting pricing strategies right is now a critical business imperative for banks as revenues dip and the business of banking gets tougher. But will age old pricing strategies continue to be effective in a modern banking market? And how can emerging technologies like Artificial Intelligence and Machine Learning to improve their pricing strategies?
Deciding on a pricing strategy has been one of the biggest challenges for businesses across sectors, banking included. The pricing approach is usually factored around expected outcomes as well as some key considerations. These include profitability, market share, the competitive landscape, consumer perception of price, revenues, and profit projections, making pricing a complex challenge. The complexity is further compounded by the fact that to be effective, pricing cannot be static and must change depending on changes in the macro environment.
Prices are usually changed based on the past performance after periodic reviews. But this is time consuming and risky as it involves past indicators which may not be valid for future pricing requirements. Banks need to move quickly to adjust pricing and even correct wrong decisions. Traditional pricing processes cannot work at this pace and are also prone to human error. The good news is that cutting edge technologies are now available for banks to leverage for effectively solving the pricing dilemma.
The banking sector has been leveraging Artificial Intelligence to enhance certain functionalities for a while now. In the early days, the use of AI was restricted to customer facing functions in the form of chatbots and virtual assistants. The HSBC humanoid, for example, handled only customer support activities such as helping customers to open or close accounts, and relaying credit card details. But over the last few years the sector has been expanding the role of AI to finetune and improve middle office tasks like fraud prevention, customer segmentation, KYC verification, credit underwriting, and lending risk management. For example, solution providers like Simudyne use AI to assist financial institutions to run large scale stress test analyses.
The emergence of digital native fintechs and technology giants has opened up a new era of hyper personalized banking. While most traditional banks have already embarked on their digital transformation journeys, they now must consider deploying AI platforms if they want to meet the hyper personalization challenge. As the custodians of vast realms of customer data, banks have a distinct advantage over fintechs, and AI can help them unlock the value of this data.
With a state-of-the-art AI platform banks can use contextual insights from customer data to provide a superlative customer experience and a level of personalization previously unseen in the banking sector. AI platforms can analyze data across silos to understand customer behavior, usage of services and even willingness to pay for products and services. They can then calculate prices based on micro-segments and allow customers to compare prices in real-time. But to tap into the full potential of AI-based pricing, banks need to have a clear understanding of their business needs and their pricing priorities. They also must put in place a stringent governing process to ensure maximum data security.
In its next stage of evolution, AI is bound to reinvent the way pricing is done across the banking industry. This is the time for the banking sector to step in and utilize AI’s power to produce better-priced products that cater to customer needs while ensuring profitability in a competitive landscape.
