NAPEO
Nothing seems untouched from AI these days. Even PEO risk management. Earlier this year, I had the privilege to join a panel at NAPEO’s Risk Management Workshop on the growing influence of AI and how it impacts the world of PEO risk management.
Insurance, healthcare and employee benefits have common business drivers that mandate the need for test automation. Each of these industries are data intensive and often include personally identifiable information (PII), payment card industry (PCI) or protected health information (PHI) compliance. There is no room for manual errors. While that alone may be sufficient to justify test automation, the customer experience lifecycles for onboarding, servicing, and renewals drive successful customer satisfaction and retention results. This is where PEOs can differentiate themselves with automation.
In today’s digital environment, test automation is necessary for meeting the service levels required by a sophisticated customer base. Given the wider adoption of agile methodologies, automation, and continuous deployments, testing cannot be executed using traditional manual processes. In this article, we will examine key opportunities for introducing test automation and identify approaches and technologies to drive successful project delivery and ensure accelerated onboarding of customers.
DOES ONBOARDING TEST YOUR CUSTOMER’S PATIENCE?
For employee benefits, onboarding involves various insurance and healthcare products and payroll options. A tremendous amount of active and historical data is transferred between customer, broker, and administrator. To add to the challenge, the quality of the data is often problematic and may come from various sources. Organizations can achieve automation by building testing accelerators, data extraction engines, automated forms and file comparisons, and automated reconciliation into your process. The onboarding process sets the bar for service expectations or can build a backlog of issues that may take weeks or months to resolve and start the relationship on a challenging path.
UNDERWRITING: ENSURING THE PRICE IS RIGHT
For property and casualty and specialty business insurance, submission and underwriting process automation is a key component of winning business and issuing policies. Testing must align with a straight-through-processing model. Processing ingests product-specific forms, claim loss run reports and supplemental data. Accelerators can be built for identifying specific forms for data extraction, testing automating risk triage and ensuring the correct pricing is applied based on accessing a rating engine or applying an underwriter decision. Testing the correct data ingestion, risk triage and raters are key to getting the proper pricing for the risk class, notifying the broker, and issuing the policy. How you price the risk will drive the profitability of the business.
TO RENEW OR NOT RENEW THAT IS THE QUESTION
Open enrollment is the time where test automation is often appreciated most. This is a time where organizations are onboarding new customers while also renewing existing customers that may have selected various new products and services along with revised pricing that aligns with prior claims and service experience. For renewals, automation can include the analysis of data collected during the contract year and testing the implementation of the changes in product or benefit selection, third-party interfaces, reporting, and premium billing for the new effective date. The windows are tight to sunset prior year rules and implement new contract year selections. Automation is a key enabler to avoid a backlog and provide a positive customer experience.
SELECTING YOUR AUTOMATION FRAMEWORK
At Quess GTS, we use a variety of tools for automation. Three commonly used tools for test automation are Selenium as an Automated Testing Framework, Jenkins for Continuous Deployment and Excel for building Test Case Accelerators. These tools work very well together and are ideal for organizations of all sizes. The frameworks integrate with existing tools and technologies without constraints on scale. They also provide comprehensive reporting and analytics to track test results, defect trends, and enable prioritization.
With Excel, you can generate hundreds or thousands of test cases or easily modify your test case repository for similar conditions. For insurance, healthcare and employee benefits you may want to consider starting with:
BUILDING YOUR TEST CASE REPOSITORY
Automation will not happen overnight. It is an incremental process that leverages reusable cases and assets for onboarding new customers, testing new features, performing automated compares, or running regression. The following are considerations in building your repository:
TESTING YOUR AGILITY
Test scripting in an agile environment requires writing automated, repeatable, and reusable test cases. Test scripts must be well documented and easily adaptable for frequent or continuous deployments. Some considerations include:
Maintainability and scalability: Automated tests in Agile should be easily updated to accommodate frequent changes.
Collaboration and communication: Agile processes require collaboration among testers, developers, and product owners. There must be clear targets, alignment on goals and defined acceptance criteria.
Continuous Deployments (CD): Automated test scripts should run as part of automated process that may evolve to a CD pipeline, providing immediate feedback on the quality of code changes in a release.
User Experience: Test scripts are not for back-office functions only. The user experience should consider multi-platform/browser inputs (mobile, desktop, other), workflow rules and straight-through-processing while ensuring security requirements are met.
While each organization is at a different level of technology and test automation sophistication, test automation is achievable for all. Using some of the tips and recommendations in this article within a PEO industry context enables a verticalized approach to automation. Organizations should start small, scale the automation, and consider evolving to continuous deployments where test scripts are bundled with software changes into a shared environment. With each update, automated builds and tests are triggered for early detection of errors. Integrating automated testing seamlessly into the delivery pipeline provides actionable insights to each release, enabling quick defect resolution and limiting technical debt. The result is improved quality, time to market and customer satisfaction.
Test automation is a mandate in today’s digital-first environment and provides a PEO a foundation to grow its business while increasing the competitive capabilities to win and service business. Our recommendation is to start with test case automation and frameworks and transition to CD over time based on business needs, technology tools and application stack. We suggest partnering with a firm with expertise in this area to assess your environment, collaborate with your team to build test cases, provide advisory tools and establish the environment, and ensure scalability to increased automation and potentially continuous deployment. The right approaches will give you a time-tested digital foundation to grow your business with significant improvements in customer experience.
AI-assisted hiring represents not just a trend but a significant shift in how organizations approach talent acquisition, promising a more efficient, unbiased, and inclusive future.
The bottom line – if you have not updated your CCPA notices since 2022 or earlier – or if you have never provided such notices – you should act quickly to implement new notices and stay compliant with the ever-changing law.
As the rate of digital transformation continues to accelerate across all industries, insufficient cybersecurity guardrails and policies remain a pressing threat to the insurance and benefits ecosystem. Here are 6 questions to ask to assess your PEO’s cyber risk.
Deepfake media is only going to become more prevalent. Its use in social engineering is going to grow. Education is the best way to help your organization thwart this alarming reality.
From my vantage point, the infusion of AI into the unemployment sector holds the key to unlocking a multitude of advantages that could revolutionize the efficiency, accuracy, and cost-effectiveness of unemployment insurance operations.
Efficiencies are and always will be an investment, and updating your daily processes may take time; however, the payoff is guaranteed. By innovating with technology, you will achieve your goals of improving efficiencies and delighting your customers.
PEOs should seek to cultivate a culture of risk management surrounding AI use – both in your organizations and at your clients’ places of business – in order to be best positioned in this new area. These are the 10 most critical steps to minimize.
Today, any business that doesn’t capitalize on data advancements risks being swept away with the tide. PEOs in particular should think of data as a direct pipeline to the resources you and your clients need to navigate the complex world of HR and compliance, and make better business decisions in the process.
AI holds the potential to revolutionize the insurance and benefits industry, contributing to risk reduction, enhanced customer service, and augmented efficiency. The potential of automation and generative AI is boundless, and adoption continues to surge.
Automation tools can provide some much-needed relief during open enrollment, empowering small business leaders to get it all done. However, many small business leaders seem unsure about the return on the investment of technology in this space.
We’ve now reached a critical juncture where companies need to make a few important decisions about how to use AI. Many topics will include both HR and HCM, meaning countless companies will be looking to their PEO partners to provide guidance in navigating the increasingly complicated AI waters.
The pressing question for organizational leaders centers around how to leverage data strategically and productively toward meaningful results. Here is a simple framework to drive value and impact through the use of quality data.
PEOs should be aware of what AI tools can do to assist their clients but should also recognize that this is an emerging technology still subject to flaws.
When I was young, I watched movies with robots and drones; we called this science fiction because such things would, of course, never come true. This was only imagined to be future scientific or technology advances as is artificial intelligence or AI. So here we are – and yes, robots and drones have come to offer technological advances that can enhance the way we interact with each other and with the world. And they are here to stay!
So how can AI impact mergers and acquisitions (M&A)? What M&A tasks can a robot control or computer complete in the M&A world that are usually done by humans? Let’s look at just a few examples.
LEVERAGING AI IN M&A TASKS
Target Screening.To determine the best ROI, shareholders of the buying company must identify acquisition targets and understand how the deal will impact their strategy and financial performance. By harnessing the power of AI, buyers may be able to more efficiently and accurately identify potential targets, thereby increasing the likelihood of successful acquisitions.
Due Diligence. AI can streamline the due diligence process by document review and analysis. Cloud based data rooms have already revolutionized M&A due diligence by replacing physical data rooms and I think AI will enhance the process even more. And to think that back in the day we had real paper deal books that we mailed!
Analysis of Information. AI may be able to analyze information such as a company’s brand, management, trajectory, resources, productivity, and financial information to determine the profitability of the combined entities.
Reduction of Risk. AI may be able to reduce risk in due diligence by analysis of huge volumes of data, therefore forecasting trends. This could help decision making for more successful MA strategies. Companies can leverage AI as algorithms accurately aid better predictions which makes a shift in how deals are originated and evaluated.
Valuation. Determining the value of a PEO is made of many distinct pieces of the valuation puzzle. I just do not see a tremendous impact on the human ability to understand valuations across the PEO industry unless they have done multiple deals and understand the comparisons in detail.
Post-acquisition. AI can follow an acquisition, facilitating the integration by automating various tasks including data migration, employee onboarding, and process standardization.
In summary, AI is reshaping the way buyers undertake due diligence, make decisions, and integrate post-merger. Organizations can obtain profound understandings of target companies, minimize the duration and expense of M&A and make better informed decisions driven by data.
But at the end of the day there is one thing you cannot take out of successful M&A transactions and that is the people. It takes a combination of business and emotional intelligence to be a great M&A advisor, and it takes an incredible “read” on the people involved on both the buy and sell side to know if a deal will ultimately be successful. It is kindness, integrity and respect that truly guide the M&A process and those for me are simply real-time and human. So for now, while some of the deal tasks can be completed by AI, great deal making is about the ability to understand, guide and value the people and will not gain immediately from AI in my opinion. But then again, I was not a believer in science fiction!
I have often found myself standing for minutes in my nearby Publix trying to figure out which item to buy. Did I want the $2 name brand product, or the $1.50 Publix generic version? Did I want the less tasty Gala apples at $1/lb or the tastier Honeycrisp apples at $2/lb? I would stand there paralyzed by the decision.
Thankfully, at some point, stomach pain kicks in and forces you to make a decision. But in real life, there are many in PEO leadership who are staring blankly, maybe even petrified and anxious, in the aisle of predictive models trying to figure out how to decide between different companies. So, let me do my best to present you with points you should consider to move from trepidation to triumph.
First, two caveats. The first one is that this is not comprehensive. Writing a thorough treatise on this would take too long and I’ve already used enough of my word limit. The second caveat is that this article is focused on analytics firms providing underwriting, pricing, loss control, or claims-level solutions that are commonly marketed as AI.
So, how does one select an AI vendor in claims, loss control, underwriting, or pricing?
CONSIDER THE BUILD VS BUY DECISION
There are a lot of considerations going into this decision, but it’s worth checking this point off before you start heading to the analytics store. Building something in-house or even with outside support could be a better option if you’re looking for a more customized solution, have in-house technical expertise and resources, and/or need a quicker turnaround. The customization aspect is important because PEOs are not insurance companies, and so you’ll want to find a vendor that can not only spell P-E-O, but also know how a model implemented with you would differ from an insurance carrier.
It is worth noting that I have definitely seen companies start off with great intentions and then end up with more money spent and less of a product than if they had just outsourced the work from the get-go. Going with an external vendor can mean avoiding diverting too much attention and resource from other parts of the business, having immediate access to specialized expertise, proven technology, and on-going support.
HAVE THE RIGHT PEOPLE LEADING THE RFP
A request for proposal (RFP) process or something akin to it is absolutely crucial to ensuring a methodical approach to evaluating the potential vendors. Internal stakeholders with previous experience are the most ideal, and it’s important to also incorporate other stakeholders if not into the actual process then at least to get feedback at the outset.
Experience is key but a characteristic not to be overlooked is independence. Whomever you have leading the process, make sure that that person or group of people have the best interests of the company at heart, with no selfish motivations apparent that might impair their judgment.
LOOK UNDER THE HOOD
It’s important that you, or the people you trust, understand the model design and how it compares against competition. I’ve seen models promoted as being robust and cutting edge, but once you get through an NDA and delve into the model details the model is shockingly dated and not well suited for anyone’s needs.
It’s also good to give it a test drive. How does the model perform on your own data? Having real-life examples can help you get comfortable with important details like the potential range of ROI and what a model “error” or incorrect prediction might mean financially and operationally.
DIFFERENTIATE BETWEEN SALESPEOPLE AND POST-SALE SUPPORT
As you go through the RFP process, some firms will connect you with smooth-talking salespeople and flashy decks persuading you that your hard-earned budget will generate an immense return. However, after you sign the contract, you quickly realize and regret that the immediate attention you received from that salesperson is now replaced with inattentive and rigid interactions with the people actually doing the work. As you go through the RFP process, make sure you differentiate those two groups, and talk to the actual people behind the scenes.
A subpoint to this is understanding how important of a client you are for the company. Are you a smaller or larger client for them? Will they be willing to work with you on changes in the model because you’re a valued client, or will they brush you off because you’re booked as a win on last month’s sales quota?
PUSH FOR PERFORMANCE-BASED CONTRACT TERMS
It’s great when companies say they want to share in your success and ensure there will be little downside to spending your budget with them, but what if they were able to codify those assertions in a contract? Having a contract designed such that you pay little when there’s little to no results, and you pay more when you’re swimming in ROI is a true partnership. Of course, the devil is in the details. You have to not only define “success” but be able to objectively quantify and monitor it over time.
While there are many other considerations at play and each situation is different depending on the PEO’s history, sophistication, needs, goals, etc., the above points hopefully will help reduce some anxiety when looking at the different options in the predictive modeling aisle.
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As you might expect with cybersecurity, battlelines are being drawn between the people creating AI solutions to help protect companies and the people making AI software that is designed to find vulnerabilities in areas designed to protect data; systems; financial and personal information; intellectual property (IP); and Industrial Internet of Things (IIoT) and other IoT devices.
As you might expect with cybersecurity, battlelines are being drawn between the people creating AI solutions to help protect companies and the people making AI software that is designed to find vulnerabilities in areas designed to protect data; systems; financial and personal information; intellectual property (IP); and Industrial Internet of Things (IIoT) and other IoT devices.
You might think your PEO and most of your customers don’t need a workplace policy covering artificial intelligence (AI). After all, you and your customers might not have an internal AI product for workers to use, and not all your customers are in the tech space. But employees across all industries are intensely AI-curious and are wondering whether ChatGPT, Google’s Bard, and other similar platforms can help them at work. With this inevitable use comes some serious legal risks for your PEO and your customers. An AI policy can reduce some of the risk. What are the top 10 reasons why you and your customers need an AI policy?
Experts have written articles for decades urging companies to update their business with cutting-edge technologies. By now, you may have been lulled to sleep by such headlines, content with your current processes and an “if it’s not broke” mindset. You may even be convinced that recent technological advancements like artificial intelligence (AI) are just a fad or something to ignore. However, given the exponential rate at which new cutting-edge technology is transforming every industry – including the PEO industry – you and your business no longer have the luxury of complacency.
Paul Nash is an employment practices liability (EPL) underwriter with Beazley. He is the EPL and Safeguard product leader for both the UK and US teams and was instrumental in developing the first SAM/SML policy issued by Beazley in 2006. He has more than 30 years of experience in the insurance. He recently spoke with Paul Hughes of Libertate Insurance about the state of the EPLI market, how he has seen the PEO industry evolve and more. PEO Insider captured their conversation.
When it comes to signing up new employer groups in your PEO’s health insurance pool, collecting questionnaires has always been a sticking point. Today’s clients are like any other consumers; they demand seamless and nearly instant purchase experiences.
