Financial Services

Strategic cost reduction in financial services:
Spend intelligence and analytics

Just like service operations, spend intelligence and analytics are among the most important areas to focus strategic cost reduction initiatives. Banks can significantly cut overheads by improving how they monitor spend by adopting better analytics capabilities and processes.

This won’t merely save money. When approached correctly, optimizing spend intelligence and analytics will make the overall business more efficient and actually lay the groundwork for further digital business transformation (DBT).

Enhancing analytics promises a multitude of additional benefits.

Financial institutions spend billions of dollars each year on non-compensation expenses, essentially everything that’s not employee salaries and benefits. This includes the procurement of supplies and services for their workforce, such as IT, printing, computer monitors, travel, real estate, temporary labor, etc.

Although most banks already have procurement organizations dedicated to cutting a percentage from that bucket of spend each year, they could make far more progress with a data-driven approach.

It’s not enough to team up with a consultancy that only offers spreadsheets and conversations. Banks need analytically driven digital solutions and the resources to execute them effectively.

Digital transformation partners like Publicis Sapient can set up advanced analytics tools and other new capabilities to uncover many prospects for cost reduction and establish best practices for driving efficiencies.

After they’ve been implemented and run successfully, these data and monitoring tools can be handed over to the banks to run on a weekly or monthly basis – continually fine-tuning expenditures.

The goal is identifying savings and prioritizing those.

Every major CEO in financial services has already said they would not lay anybody off in 2020. But they are virtually bound to leave billions of dollars on the table because they don’t have a clear view of vendor spend and cannot manage demand against that spend.

Machine intelligence can identify costs that are not readily apparent by looking at the accounting system with human eyes.

For instance, Publicis Sapient uses a dashboard that tracks all the payments that a company is making and consolidates that data for a clearer view of where the money goes. Users can manipulate the data and get a comprehensive look at the whole picture.

Many institutions already have DBT programs in place that have not delivered the anticipated benefits. Cutting costs through improving analytics has the ancillary benefit of informing and improving DBT strategies.

Most people approach DBT by thinking of a digital-centric idea, designing a business case around it, building the product and executing. This is not the way

to go.

By looking at non-compensation expenses, banks can take a smarter, more deliberate approach to cost reduction. The data uncovered through cost analytics-driven cost reduction can prioritize and accelerate digital initiatives.

For instance, a bank may have a plan to swap commercial print for online forms over the course of five years. But a sophisticated audit could reveal that they would save around $15 million by fast-tracking that transition.

In late 2019, it would have been inconceivable to the public that major banks might one day forego brick-and-mortar bank branches entirely. But after the COVID-19 pandemic, this possibility seems entirely plausible. An institution could use proprietary data and AI to identify where they legitimately need bank branches and where the retail and cleaning costs would be worthwhile.

Let’s say this process finds that a bank spends millions of dollars each year on junior level employees traveling to company sites on a regular basis. It may be as simple as transferring those meetings over to video conferences. If not, the leadership team can join a DBT partner to determine which digital tools could help facilitate their jobs without the airfare.

In all these examples, digital interventions can either replace physical requirements or change work processes to reduce the demand on third-party expenses. A design-led approach to driving digital adoption would sustain these savings over time.

This is somewhat confounding to financial institutions because suppliers have far more people trying to make banks spend more money than they have dedicated to cost reduction. This asymmetry overwhelms buyers. They may launch a project every five years or so in efforts to reduce these costs, but they aren’t linked to anything else.

This is an opportunity to change all that. Rather than having a bunch of humans trying to deflect suppliers, financial institutions can use machine intelligence to pull together all of that fragmented data.

With better information, banks can make better decisions about where to cut and where to invest.