Is your digital transformation program on track to improve your bank’s profitability? While all banks today have a digital transformation initiative — in many cases, several such initiatives — the question is often difficult to answer. How do you draw a clear line from digital transformation to customer success to overall bank health and profitability? And what can you measure along the way to ensure you’ll get the payoff you need?
I have just published an article in The Journal of Digital Banking addressing this question. It explores the success factors for improving your bank’s efficiency ratio, and shares case studies showing how banks made these improvements by using FICO Platform, an enterprise intelligence platform for authoring, managing, automating and improving customer decisions.
To start with, let’s look at what I think should be a goal for any financial institution's digital transformation effort: improving your efficiency ratio.
Measuring Your Efficiency Ratio
In my experience, when it comes to digital transformation, ‘investing’ does not automatically equate to ‘succeeding.’ In recent reports, I read that 87 per cent of major corporations have a digital transformation effort underway (IDG), and to date only 30% of companies have achieved their full desired results (Boston Consulting Group).
Efficiency ratio is an industry-accepted calculation that scores a bank’s profitability, an important measure of its financial stability. The goal is to have a low score, as in golf, not a high score, as in bowling. A low efficiency ratio indicates that a bank is spending less to generate every dollar of income. Top-performing banks strive to keep their efficiency ratio under 60, ideally below 50, implying that every US$1 of expenses results in US$2 of revenue, which is considered optimal.
Banks calculate their efficiency ratio by dividing their expenses by net revenues, determined by subtracting loan loss provision from operating income.
Efficiency Ratio = Noninterest Expenses/(Operating Income – Loan Loss Provision)
For example, if a bank has net revenue of US$100m and expenses of US$65m, the efficiency ratio would be
US$65m/US$100m = 0.65 = 65%
In this example, 65 is a good — but not great — efficiency ratio; ideally, I would encourage a client with a score like that to use their in-house data to focus on more efficient customer acquisition, service and retention strategies, such as offering new financial products, cross-selling and up-selling efforts focused on existing customers, which can be done at a dramatically lower cost than new customer acquisition programmes.
Nine Success Factors that Help Drive Efficiency Ratios Lower
Having an end-goal like improving your efficiency ratio is important, but to meet that kind of target you have to understand how a digital transformation program can make this happen. How can you set up more specific objectives to guide your work and measure your progress?
In FICO’s experience with banks around the world, we have found nine success factors for digital transformation projects that address business goals.
1 | Expanding lending through improved pricing and risk assessment using previously siloed data across divisions |
2 | Driving down long-term structural IT costs through the use of cloud innovation and more flexible technology solutions |
3 | Quickly assessing and seizing strategic and tactical opportunities in an increasingly volatile economic climate |
4 | Reducing losses through next-generation collections and recovery capabilities |
5 | Improving the customer experience by delivering effective targeted omnichannel communications and AI- informed customer relationship management (CRM)/next best action |
6 | Rapidly deploying analytic advancements using new data sources |
7 | Moving important decisions to real time, including the continuous evaluation of customer exposure |
8 | Maintaining regulatory compliance through a customer-level view of decisions, preferences and responses |
9 | Adopting enterprise fraud management, minimising the cost and negative customer experience of multichannel fraud patterns like account takeover (ATO) |
Let’s dig into each one of these.
Success Factor 1: Driving additional lending and improved risk management through enhanced decisioning using previously siloed customer and product data across division
Emphasis on: Revenue generation
Problem statement detail
- Each division holds data on a single customer in siloes, which cannot be seen or used by other divisions in their customer decisioning.
- Suboptimal decisions are often made, which can lead to an inconsistent customer experience depending on the divisions they interact with.
Capabilities needed
Connected/centralised decisioning, decision modelling, data orchestration, origination management, customer management and optimisation.
There are often large overlaps in data requirements for decisions made in siloed platforms. By using a common platform, the need to maintain data in multiple platforms is eradicated.Data is built once and then used in multiple use cases. Issues identified and corrected in the first use case drive down future development time for others that follow. The future upgrading of data is simple and inexpensive, and new data sources can be easily absorbed into the decisioning framework.
Key performance indicators
Overarching Metric: Share of wallet
Detailed Metrics:
- Customer retention
- Profit per customer
- Lending per customer
- Return on RWA
Success Factor 2: Driving down long-term structural IT costs through innovative use of data.
Emphasis on: Cost control
Problem statement detail
- There is enormous pressure on top-line revenue, which is putting pressure on efficiency ratios.
- Banks are pulling back on marketing and reducing investment budgets; however, there remains a need to drive longer-term structural cost reductions, for example, reduced full-time equivalent (FTE).
- High levels of mandatory/regulatory IT change and ongoing maintenance costs are resulting in lower levels of discretionary investment budget, which is typically 4–5´ oversubscribed.
- Banks need to reduce their reliance on IT area by centralizing data and putting control into the hands of business areas.
Capabilities needed
Connected/centralised decisioning, data orchestration, origination management, customer management and debt collection and recovery
A platform approach eliminates the need for ongoing upgrades and maintenance costs of legacy systems.
The platform moves control of change from IT to the business, enabling cheaper, faster response to changing market conditions.
Key performance indicators
Overarching Metric: Cost: Income ratio
Detailed Metrics:
- Revenue Per FTE
- Running cost per account
- Cost per application
Success Factor 3: Pivoting strategic and tactical initiatives rapidly through an increasingly volatile economic climate.
Emphasis on: Revenue generation/cost control
Problem statement detail
- We are reactive to changes in our environment and are often slow to change when a crisis hits us. While we reacted to COVID-19 at pace, it was at the expense of many other initiatives in the organization.
- We have limited ability to model and devise strategies for different scenarios that can be stored and deployed at pace.
Capabilities needed
Connected/centralised decisioning, decision modelling, data orchestration, origination management and customer management.
The availability of multiple data sources enables rapid strategy development and enhancement of existing strategies.
Key performance indicators
Overarching Metric: Number of changes per annum
Detailed Metrics:
- Average speed of change
- Reduced IT change cost
Success Factor 4: Capturing value through next-generation collections and recoveries capabilities
Emphasis on: Cost control
Problem statement detail
- Banks are operating on aged collection infrastructures, predominately mainframe based, nonintegrated across product lines, composed of multiple vendor applications with some functionality no longer enhanced by the vendor.
- There is limited ability to deploy new models and segmentation. A lack of integration does not support customer-level treatment.
Capabilities needed
Connected/centralised decisioning, optimisation, debt collection and recovery, customer management and customer communication.
Key performance indicators
Overarching Metric: Provision Expense
Detailed Metrics:
- Increased capacity
- Decreased charge-offs
- Increased offer take-up rate
- Reduced call agent call times Improved customer outcomes
Success Factor 5: Delivering effective communications as consumers’ expectations of banks to provide omnichannel experiences rise.
Emphasis on: Revenue generation/cost control
Problem statement detail
- As consumer preferences shift to be more digital-first, banks need to improve the relevance of digital communications and guide consumers based on their interactions and usage.
- By providing customers with relevant content focused both on servicing and product needs banks will drive increased customer engagement and reduced costs to service.
- Using next best action models fed by real-time data, banks want to drive customers to self-serve online for low value adding transactions, enabling our face-to-face channels to focus on more complex customer cases, for example mortgages, investments.
Capabilities needed
Connected/centralised decisioning, data orchestration, optimisation, customer communication and marketing. Operationalisation of decisioning strategies with omnichannel, interactive customer communications in real time, underpinned by a 360-degree single customer view across the bank.
A cloud-based platform enables easy interface with internal and external assets, giving agility to deliver new products and services at pace.
Key performance indicators
Overarching Metric: Net Promoter Score
Detailed Metrics:
- Cost to service accounts Complaints per 1,000 accounts Reduced attrition
- Customer engagement scores
Success Factor 6: Rapid deployment of analytic advancements using new data sources
Emphasis on: Revenue generation/cost control/compliance
Problem statement detail
- Banks are constantly seeing new sources of data being made available with claims that they will all improve the effectiveness of decision making.
- Assessing these data sources, simulating their impact and operationalizing them into our data infrastructure is resource intensive and time-consuming.
- Many of these data sets never make it onto our road map, and so banks are at risk of falling behind the competition.
- Once your analytic teams can solve a problem, deploying the solution should be the least of the effort. In most organizations, analytical advancements sit on the shelf because data cannot be accessed or the technology cannot be deployed close enough to the decision.
Capabilities needed
Connected/centralised decisioning, real-time data streaming, data retrieval and mapping, analytic authoring, decision modelling, data orchestration, customer management and origination management.
A cloud-based platform enables easy interface with internal and external assets, giving agility to deliver new products and services at pace.Rapid assessment and integration of new data sources as they become available ensures competitive advantage from a decisioning perspective.
Key performance indicators
Overarching Metric: Cost of change
Detailed Metrics:
- Number of new models deployed
- Number of strategy changes Marketing effectiveness
Success Factor 7: Moving important decisions to real time, including the continuous evaluation of customer exposure
Emphasis on: Revenue generation/cost control
Problem statement detail
- Shifts towards a 365 × 24 × 7 model for consumers to interact with the bank via digital channels are resulting in an increasing need to be able to continuously underwrite a consumer’s position using real-time data and decisioning based on a customer’s latest interactions.
- Many processes still work in a batch environment, which do not take into account the latest consumer situation.
- Banks need data and decisioning infrastructure that takes the very latest consumer situation into account using both internal and external data.
Capabilities needed
Connected/centralised decisioning, real-time data streaming, data orchestration, decision modelling, origination management, customer management and debt collection and recovery.
These capabilities reduce the learning loop duration, as strategies can be developed and deployed rapidly. Innovative data orchestration and streaming provide real-time feedback and reduced time to ROI.Operationalization of decisioning strategies with omnichannel, interactive customer communications in real time is underpinned by a 360-degree single customer view across the bank.
Key performance indicators
Overarching Metric: Profitability per customer
Detailed Metrics:
- Impairment metrics, for example, bad/charge-off rates
- Risk/reward trade-off
- Reduced cost to serve
Success Factor 8: Maintaining regulatory assurance through a customer-level view of decisions, preferences and responses
Emphasis on: Compliance
Problem statement detail
- Parallel deployment across lines of business and life cycle of regulatory or compliance requirements is costly, error-prone and difficult to audit and control.
- Duplicative attempts of the same regulatory objective are inefficient and lead to increased exposure to regulatory breaches.
Capabilities needed
Connected/centralised decisioning, analytic authoring, data orchestration, decision modelling and decision asset inventory management.
A modular approach to decisions, which occur in full knowledge of the customer and their products, centralizes and reduces the need for repetitive compliance checks.
Key performance indicators
Overarching Metric: Cost of governance, reputational risk.
Detailed Metrics:
- Number of regulatory breaches
- Cost of fines
- Reduced regulatory penalties
Success Factor 9: Adopting enterprise fraud management, requiring the breaking down of the channel/siloed approach to detecting fraud.
Emphasis on: Revenue generation/cost control/compliance
Problem statement detail
- Fraudsters are determined, clever and adaptive. This requires a complete view of their organization, looking for fraud risks and patterns across channels, departments and functions.
- Many banks have multiple systems for managing fraud on different products, and these often duplicate functions used to detect financial crime (anti-money laundering (AML)).
- The risks to an organization can be profound. Financial assets, trade secrets and organizational reputation may be compromised. More systems, more digitization and more data provide the fraudster with ample opportunity to find and exploit the vulnerabilities.
Capabilities needed
Connected/centralised decisioning, data orchestration, enterprise fraud and financial crime management, origination management and customer management.
Key performance indicators
Overarching Metric: Credit and operational losses
Detailed Metrics:
- Impairment metrics, for example, charge-off rates
In my next post, I’ll review seven banks that are achieving big gains in their efficiency ratio across these nine success factors.
How FICO Can Make Your Digital Transformation More Successful
- Read my full article from The Journal of Digital Banking, “How Financial Services Leaders Are Using Enterprise Intelligence to Optimise Efficiency Ratios”
- Explore FICO Platform capabilities
- Watch videos of our customers talking about their success with FICO Platform
