Banks face a multitude of challenges these days. In part one of this series on pricing, we outlined some of the business challenges banks face. We also dived into the current state of retail and corporate banking and highlighted why an age-old pricing strategy for the banking sector is one of the biggest roadblocks to competitiveness.
In a blog titled ‘In 2021, Banks Must Sustain the Digital Momentum’, Forrester points out that in 2021, banks will face increasing demand for digital experiences, and customer profiles will begin to skew towards the "tech-savvy" user or the "early-adopter" customer.1 Digital transformation remains a valid goal. However, it needs backing from a robust, relationship-based pricing model.
Legacy pricing models combined with outdated infrastructure are hobbling banks and preventing complete digital transformation. Here are five areas that banks need to tackle to capture the opportunities presented by the digital era.
Many banks adopt a cost-plus pricing model that allows them to set prices a few points above costs and capture steady margins. While this model takes care of costs, it leaves potential profits on the table. In this case, customers are never differentiated, and the value of a relationship is never quantified.
Some forward-thinking banks have implemented machine-learning algorithms to help them determine the best way forward. However, in its article titled ‘How Machine Learning Can Improve Pricing Performance,’ McKinsey and company noted that repricing services in this way usually destroys as much as it creates value.2
A simple explanation of this situation is that banks are disconnected from their customers' needs. No amount of data collected, or analytics derived from this data can help a bank when the focus is on achieving cost-plus efficiency.
Interest rates in retail banking are also a good example. Banks either offer fixed interest rates above a government benchmark or experiment with variable rates. Inevitably, banks tend to revert to cost-plus since managing these variable-rate workflows is too complex.
Discounts are applied incorrectly, and tracking is haphazard since rates become disconnected from customer value. Thus, banks remain stuck in a cycle of un-competitiveness despite transforming digitally.
Pricing in a modern bank is a siloed process. A central asset and liabilities team often issues broad guidelines, and individual branches are expected to quantify customer value and issue prices. Branches often lack the tools and expertise to price intelligently, and the result is fragmentation.
A relationship manager at a branch often bears the brunt as customers inquire about different prices offered at different branches of the same bank. In some cases, a relationship manager’s prejudices and biases affect the final price a customer receives. The result is that customers view their banks as dysfunctional and lacking coordination.
In an age where customers have become accustomed to hyper-personalization, presenting a siloed approach won’t win the bank any favors. While installing a central pricing strategy is helpful, banks need to first dive deep into customer needs and expectations.
From here, banks must measure the value of every relationship they have, accounting for risk and opportunities. Quantifying the value of a relationship will help branches and relationship managers understand the bigger picture. It also eliminates data silos and the outdated branch banking model that frustrates customers.
While corporate banking takes risk into account in pricing structures, their teams often lack visibility into relationship values. This is because of legacy system challenges or the lack of metrics to define the value of a relationship. The result is that risk-averseness becomes the order of the day, leading banks right back to the inefficient cost-plus model.
Many banks fail to execute their inefficient pricing models, compounding problems further. Loan and mortgage departments are good examples. Currently, banks face stiff competition from online lenders. In a paper titled ‘Fintech Lending: Financial Inclusion, Risk Pricing, and Alternative Information’, the authors reiterate that in the United States, these technologically savvy lenders have converted traditionally underbanked customers into loyal enthusiasts.3
A bank's underwriting process involves looking at some measure of a credit score, income to debt ratios, and incorporating the bank's existing portfolio into the picture before offering a final rate. These models have some modicum of quantification but don't account for the complexity of developing an asset's base price.
In the case of loans, banks get away with this. However, when evaluating collateral or risk against credit applications, banks face significant challenges. Everything from funds transfer, capital, risk, operations, taxes, and surcharges add costs. A lack of data or a lack of integration leaves an underwriter flying blind.
All of this occurs before the customer is even offered a price. To unlock value, banks need to start segmenting their customers and offering tailored prices. In this model, data offers better insights thanks to context being present. For instance, a high-risk metric in one customer segment might not be as relevant with another segment.
Underwriters can take the full picture into account and classify their metrics according to context. The result is a price that unlocks value for both the bank and the customer and is a goal that the current disjointed state of pricing cannot achieve.
Banks implement many programs aimed at increasing efficiency and improving workflows. However, how effective are these processes? Even more importantly, how many banks monitor and track the efficacy of such programs?
In a 2019 paper titled ‘A Recipe for Banking Operations Efficiency’, McKinsey highlights an example of how disjointed a bank's efforts can be.4 They mention the case of a bank applying lean principles to account closure, boosting efficiency in the process by 20%. However, the operational cost savings amounted to less than 1% of the bank's bottom line. Ironically, the effort to improve efficiency increased costs since an inordinate number of resources were spent on an insignificant portion of revenue.
If banks have issues tracking in-house workflow efficiency and implementing cost-effective programs, there's a good chance monitoring promotional pricing effectiveness will be similarly affected. The siloed pricing structure often leads relationship managers to offer discounts in hopes of hitting incentive levels.
However, offer too many discounts, and a bank will end up losing value in the relationship. Throw in a lack of customer segmentation, and banks face a recipe for disaster. Relationship-based pricing helps banks align themselves around an oracle. This could be a metric or a group of them that guides all pricing decisions.
When aided by robust data analytics, banks can react dynamically to market conditions and price effectively at all times. Best of all, customers will always feel valued since the strength of their banking relationship is always considered first.
Relationship-based pricing currently exists in small pockets at certain banks. While this is a great move, the issue is that most banks’ IT infrastructure is legacy oriented. New solutions tend to get patched on top of older ones leading to disconnected workflows.
For instance, banking relationship managers don't have access to relationship-based pricing-related recommendations and determine prices on the fly.5 Before installing an organization-wide relationship-based pricing engine, banks must take stock of their existing technology infrastructure and ensure all data sources are integrated.
Integration will require data governance and processing guidelines, and banks must work collaboratively with their technology partners to ensure these principles are installed. As of now, technology remains a serious stumbling block, with no single solution connecting disparate data sources and providing a holistic insight into a bank’s revenue and pricing engines.
Relationship-based pricing is the best solution for many of banking’s ills. It unlocks value and drives intelligent pricing throughout the customer lifecycle. The challenges highlighted in this article are significant but hardly insurmountable. Recognizing these challenges is the first step to installing new and effective pricing models.
