Your Health Insurance Playbook: Mastering Approvals And Claims

 Your Health Insurance Playbook: Mastering Approvals And Claims - The Complete Three-Part Guide to Weathering the "Perfect Storm" of Adverse Market Conditions Banks Are Facing Now—and Preparing for an Ever-Changed, Volatile Future

These factors combine to create a threat of volatility that requires managing revenue declines and reducing costs while at the same time affecting operational effectiveness – particularly operational flexibility. As a banking executive, you are compelled to immediately address the three "stones" of operational efficiency:

Your Health Insurance Playbook: Mastering Approvals And Claims

Engaging Labs as a partner in your banking process and automation to tackle the three biggest rocks has real financial benefits.

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The numbers don't lie. Following the steps can offer banks a massive cost-cutting effect. Here are some of the numbers that the lab submitted to the banks:

Big Rock 1: How to Rationalize Your Bank's Retail Branch Network You need to make data-driven decisions to avoid over-serving markets and locations—without compromising customer sentiment. There is no turning back

While today's economy will certainly improve, the way we do business - and banking - will not. The lessons learned from the past two years - from chaos and instability - are too powerful to ignore.

Crucially, banking executives need organizations that offer “operating leverage”: the ability to grow revenue faster than costs—and, in a downturn, the flexibility to absorb large revenue shortfalls with minimal operating loss.

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The good news is that disruptive events present today's banking executives with a rare opportunity to cut costs, improve processes, and make their businesses resilient against the inevitable future shocks—all at the same time.

Across-the-board cost reductions in branch networks may be beneficial. But strategically reducing excess services can provide a competitive advantage. These efforts can be divided into the following steps:

All of this, obviously, affects the branches that remain open. "Even big rock" branch connections and reducing branch footprints will be addressed later in this article.

One of the first things a bank needs to do, when looking for the "big rock" of a retail branch network, is to study its data, re: branch transactions.

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Surprisingly, most banks do not do this. We know, because in the lab, it's often one of the first things we do. Banks will have the data, but they're not leveraging it—cleaning it, analyzing it, creating useful KPIs based on business-relevant ratios, and then connecting it all into an advanced predictive and AI analytics platform that delivers valuable insights.

The power of visuals cannot be underestimated. Executives can quickly see where value is being generated and, more importantly, where it's being wasted—usually with good intentions. The lab calls this precious waste Virtuous Waste™.

Helpful Hint: Don't wait for the IT shop to come around to address critical business functions leveraging existing data from core systems. They have their hands full with daily operations.

So, you want to find out what types of customers are served by each department. Which ones are more expensive? Which is less valuable? Likewise, you'll want to see what transactions are being made, on a department-by-department basis: Where are simple check cashings, versus high-value business loan and mortgage applications? Then you want to dive into even more detail, day-by-day and even hour-by-hour analysis. You certainly don't want employees to sit idly by. You don't even want a branch open if no one visits during certain days or hours.

Healthcare Due Diligence Checklist

Armed with this information, especially if it's viewed in predictive and prescriptive KPI analytics, you'll be able to make a concise, straightforward recommendation to improve operating hours — and even move employees — across the department. To meet the forecast demand in a department. Today, you need this capability in real time - and it's available. But it's hidden beneath the surface noise is the result of the need for in-house data transparency, the operational culture of IT, and the unfamiliarity of the lines of business with the wealth of exploitable data at hand.

Today's bank C-Suite is often torn between market pressures and the board to cut costs — and internal voices clamoring for more employees.

Labs can add clarity to this conflict. We've seen banks where retail leaders claim it was "irresponsible" to, say, have fewer than seven employees in a given branch. Yet a simple analysis of the data revealed that these employees only processed two transactions per hour. It's hard to justify the need for seven speakers when they only do the job of three. The key to accelerating this transformation is KPI data visualization through advanced predictive and AI analytics.

Here's another "big rock" in banking—one so big, we could devote an entire playbook to it. And it's a keystone of reducing—ideally, eliminating—performance differences between individual employees performing the same job.

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Many organizations use overall averages to measure employee performance. This forces employers to realize that the variation from employee to employee can be significant—perhaps 15 to 30 percent. If this is true, then it is not worth the effort of further investigation. But this is not true. Executives are surprised to see that the difference is typically 2-7x between the top performers and the bottom quartile.

When this information is presented in a visual format, it's clear that the top performers shine...and that they're getting a raw deal. They are not actually recognized, while the bottom quarter does a fraction of the work and is allowed to be considered roughly equal to the business value.

Executives are surprised to see that the difference between top performers and the bottom quartile is typically 2-7X.

Information available to banks: head count, hours of operation, types of transactions processed. Yet it is also possible to draw, fold, and view

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Information, which provides banking executives with unprecedented decision-making efficiency. The lab performs this service regularly for banks of all stripes and sizes, truly leveling the playing field: we give them the kind of data you'd think only three or four banks, nationally, could get their hands on. . This is not true.

The “power” in advanced predictive analytics platforms lies in their ability to instantly crunch data from as many sources as you can throw at it—and then present the resulting insights in dashboards with seemingly limitless deep-dive options. Allows you to paint an incredible picture. Best bank-branch landscape. Most of the time, no matter how many scenarios you try, the answer is obvious and unacceptable. The endless, regulatory debate is over.

And when we say "landscape," we mean it literally. We mentioned "external data" above. This means, for example, that US census data not only shows population and income levels, but

In population - which areas are growing (or declining), and at what rate. This is God for prophecies. Imagine, on a real map, seeing your branch network as it relates to area household income, disposable income, age group, population density, and trends. It's almost like a crystal ball. The lab delivers it quickly and regularly.

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And it's hardly a coincidence. It's easy to filter these businesses—and see results visually, instantly—by factors like revenue, employees, and industry. We know this part of the playbook is about cost reduction, but we want to emphasize

. Imagine the power of feeding this type of local business data into your commercial lender sales team's CRM database. The lab does all of this on a regular basis.

However, all this information is dynamic: when the databases we plug into are updated, all the dashboard information is also updated in real time.

Here's another advanced predictive and AI analytics tool that should whet your appetite, if it hasn't already: The Lab even draws status

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In a bank's branch network, it is measured as "per day by branch." That's a staggering level of detail that any bank executive should feel comfortable doing without. And it's "standard equipment" in every one of our banking engagements.

All of this retail banking business data can then be aggregated and broken down to create scorecards that rank individual branches within the bank's network. Here's a scorecard (anonymous, of course) that combines various metrics, including daily traffic, and then ranks departments with a composite score that can be topped at "1.0":

Here is perhaps the most interesting view. This is a quadrant created by the laboratory, which maps the bank branches along two axes: performance and growth area:

Take a look at Quadrant 4. It is a "low performance" yet "high growth area". This is a great opportunity: it says to every savvy executive: "Let's send a team of tigers over there and build this department!" Do you currently have this laser capability for your revenue boosting efforts?

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When it comes to the tough decision to merge, or even close, branches, you need reliable information to guide you.

Federal regulations make it difficult to intentionally close a branch (eg, leaving a given geographic area "unbanked").

But the local connection may be less strict. The lab recently evaluated a bank that has two different branches within a mile of each other.

Although the locations were close, the metrics for potential value were not significantly different. One was in a rapidly growing area. There was no other. We can see that the stable growth position is gradually losing customers, while the high growth position was stable.

M&a Due Diligence Checklist

Next, we hide geography-based insights. What are these branches around? For example, do you want to keep a branch that resides in a sleepy secret mall (or, worse, inside a moribund shopping mall?

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