Monthly Archives: May 2019

Is your organization ready for AI? Sort through the hype and find the most practical solution

It seems as if everywhere you turn, someone’s referencing AI. “Artificial intelligence is essential for business,” states a news article. “AI is the only way forward,” insists a colleague. And the scariest of all: “If you don’t implement AI quickly, you’ll be left in your competitors’ dust.” The constant refrain of AI, AI, AI can leave you feeling like your organization has lost the race before it has even entered it.

The truth is, AI is changing the world at a remarkable pace. And, eventually, nearly every industry and business will benefit from it. “Whether you work in retail, banking, transport or the public sector, AI will be an integral part of the way you do business in the future as it has huge potential to improve decision-making, increase efficiency and power new ways of working,” states the article How to Get Your Business AI-Ready.

But that doesn’t mean AI is the best solution for your organization right now. Implementing AI takes a massive commitment in the form of time, resources, and money. It requires a critical mass of data and properly trained staff. By prematurely jumping into a high-profile AI program, you risk ignoring the valuable tools already available and stalling other strategic projects underway. Instead, a more practical approach—one that uses software scaled to your operations—will move the most important metrics now, while developing an analytics culture that will make AI more feasible down the road.

AI has become a buzzword. What exactly does it mean?

In its prolific use, the meaning of artificial intelligence has become skewed. Some have come to view it as a magical solution capable of instantly transforming business all on its own. Others equate it with automation. Neither of these is true, however. AI requires much preparation and strategy (more on that later), and where automation follows pre-programmed rules, AI involves machine learning. AI is designed to mimic human thinking by making predictions and adjusting its processes based on new data insights. In this way, AI is quite different from many previous technological revolutions, during which technology took over specific, static roles within processes.

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Facing the challenge of reputation management in higher education

The Operation Varsity Blues scandal has heightened reputation management concerns across the higher education community. Seeing how quickly any college or university can suffer reputational damage, and how lasting that damage can be, underscores how valuable an institution’s reputation is, and how critical it is to safeguard it.

The book Reputation management: The key to successful public relations and corporate communication by New York University professors John Doorley and Helio Fred Garcia opens with a quote from Warren Buffet who addressed a group of Salomon Brothers managers in 1991 after the firm became mired in a high-profile trading scandal: “If you lose dollars for the firm by bad decisions, I will be very understanding. If you lose reputation for the firm, I will be ruthless.”

Although numerous surveys show that many leaders of higher education institutions place the same value on reputation as Buffet does, effectively managing these risks remains elusive. In fact, most cannot even define what reputation is.

Defining Reputation Risk

In the article How to Manage Reputation Risk, Nir Kossovsky addresses the definitional ambiguity directly. “From your boardroom and C-suite to the SEC and Office of the Comptroller of the Currency, everyone agrees reputation risk exists, yet few can describe it. However, this isn’t as difficult as it seems.” Kossovsky defines reputation as the expectation of behavior that is set by stakeholders. “Customers have expectations when they buy products or services, employees have them when they accept jobs, vendors have them when they partner, creditors and investors have them, and even regulators have them.” For colleges and universities, this extends to the communities that house them, the potential pool of students and parents considering attendance, research partners, and the other organizations that interact with them.

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What are the foundational elements for becoming a next-level insurance brokerage?

Organizations often miss a crucial step in their drive to acquire and implement technology as a means to remain competitive. The ability of commercial insurance brokers to leverage data and analytics to bring in new business, write policies, and provide added value to their clients is about more than selecting the best risk management information system (RMIS). To get the most out of the investment in technology and become a digital leader, a brokerage should first assess if essential foundational elements are present.

The next-level broker

A next-level brokerage is a firm that has undergone the process of digital transformation, a term that CIO contributor Mark Edmead defines in Digital Transformation: Why its important to your organization as “the acceleration of business activities, processes, competencies and models to fully leverage the changes and opportunities of digital technologies and their impact in a strategic and prioritized way.”

An anonymously attributed response to the Commercial Property/Casualty Market Index (Q4/2018) survey question, “What opportunities for commercial insurance brokers do you see?” can be also be read as a more specific description of the next-level broker. He or she is able to “maximize use of technologies and analytics to grow business and do so with reduced expenses.” Furthermore, the next-level broker has the “[i]ncreased ability to target growth in select industries via use of data and analytics.” Finally, he or she is able to “[i]dentify new ways via technology and through the use of data and analytics, to solicit, write, and service business.”

Analytics functionality is an essential component in the digital transformation into a next-level brokerage. However, the act of putting a RMIS in place (or modernizing an existing system) doesn’t mean that all expectations around analytics will automatically be met. The right mix of people and data must also be present.

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Insurers: 5 ways siloed data hurts your bottom line

Data silos not only create obstacles to effective operations, but can also directly affect your bottom line. Listed below are five common issues associated with siloed data and ways to avoid them.

1. Creates a dependency on inefficient external reporting applications

Multiple platform architecture complicates the reporting process. While third-party reporting tools can be used to analyze data across multiple systems and produce unified reports, there are costs incurred. Forrester reports that nearly half of all data professionals spend at least as much time prepping data as they do analyzing it. This inefficiency worsens in cases where reporting reveals a need to modify how data is captured or organized, forcing analysts and IT resources to trace data all the way back to its original source and then make changes.

In some cases, third-party reporting tools can also create a gulf between those who master the reporting technology and those seeking answers from the reports. In a recent interview, Christopher Ittner, chair of the accounting department at The Wharton School, discussed how this division affects the business process:

“What we are finding is that in a lot of companies, there are great data scientists and great business people but what is missing is business people who know enough data analytics to say, ‘Here is the problem I would like you to help me with.’ And then they can take the outcome from the data scientists and see how they can best leverage it. That is where we must get to in the next couple of years if we want to take advantage of the digital technologies.”

Providing users with direct access to reporting that requires no prep work solves both issues. End users can become their own data analysts and answer the business questions that apply to their work. Without the requirement to master the technical process of assembling, scrubbing, and joining data from multiple systems, reporting becomes more efficient, effective, and scalable.

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3 ways an integrated healthcare risk management system benefits the claims process

As hospitals and healthcare organizations work toward better patient care, they can begin by taking a closer look at their internal processes and technology. A reliance on disparate systems that fail to share data efficiently puts organizations at risk of falling short of the demands of modern healthcare. The Agency for Healthcare Research & Quality stated that one of the three most critical challenges facing today’s healthcare organizations in their mission to improve patient care is “establish[ing] an integrated data, analytics, and information platform, along with the necessary technical expertise, to capture a 360° view of the healthcare system.”

The healthcare claims process, too, can benefit from a single integrated healthcare risk management system. Having incident reporting and claims management functionalities working seamlessly in one platform offers three major advantages.

1. Increased efficiency and accuracy

Just as working with a single insurer is easier than working with several, integrating healthcare incident reporting and healthcare claims administration into one system can be easier than tracking each in separate systems. But unlike insurance, where receiving multiple coverages from the same insurer may not be possible, hospitals can integrate incident data and claim data with ease through healthcare risk management software like Origami Risk.

Having all data in one system adds convenience for healthcare risk managers who may have previously had to toggle between systems to follow along with the claim lifecycle—from the initial reporting of an incident to the closure of the claim. A daily reality that the article Improving Claims Management with Advanced Integration summarizes as “the need to switch between multiple software systems in order to find all the relevant information on a specific claim. It’s critical to have all pertinent data in one spot to reduce and/or eliminate this quest for data.”

Navigating between two systems also results in detrimental switch costs, the fractions of seconds that occur when moving back and forth between systems. These switch costs rapidly compound, leading to wasted time and increased errors, including misaligned data. With an integrated healthcare risk management system, healthcare risk managers no longer have to bounce between systems throughout the claim lifecycle. If an incident turns into a claim, they can monitor it or move it further along in the process without losing the original incident record.

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COI management for public entities

Managing certificates of insurance (COIs) has always been a challenge. With increasing pressure on the budgets of state and local governments, dedicating the resources required to effectively manage this process can mean sacrificing time spent on other core functions. Relying on color-coded spreadsheets and a manual review process often leads to unsustainable procedures that fail to scale and adapt as an organization grows.

COI management is risk management

Although the process can be a time-draining administrative exercise, COI management is fundamental to managing risk transfer. The article Contractual Risk Transfer Issues: Reviewing Certificates of Insurance highlights the important role COIs play in risk management, noting, “Because many liability losses occur through the transfer of risk, it has become necessary for a Risk Control Consultant to assess the hazards and controls arising from contracts and agreements in a fashion similar to identifying other hazards, such as exposed wiring or missing guardrails.”

Most public entities are obligated to carefully monitor COI compliance in order to control unidentified risk transfer. Yet the administrative burden associated with endless cycles of hunting down updates and monitoring for expirations or deficiencies can easily exhaust any department. Given the mandate public entities have to stretch every resource to the furthest extent possible, the tension between the importance of an effective COI management process and the toll it takes on those managing it is difficult to resolve.

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