The current economic environment—shaped by
But like many data-driven solutions, process intelligence can seem like little more than a buzzword to those who don’t understand what it is or how to execute on the data outputs it produces. At a basic level, process intelligence involves a company harnessing data from its systems of record to identify various transaction pathways and understand its processes in more granular detail. For example, each transaction and process in insurance typically represents a part of the customer journey. Understanding each of those transactions individually allows organizations to get to the root cause of where gaps and inefficiencies in those processes may exist. Not only can process mining lead to operational efficiencies, but it can also provide insightful information to drive a better customer experience.
Insurers that want to use process intelligence capabilities to their fullest extent cannot take a “set it and forget it” approach. Process mining is the first step in developing data-driven solutions. The pinnacle of what process intelligence can enable is in taking an organization from reactive to proactive. Being proactive in monitoring and measuring process improvements can help companies understand what strategic, operational and talent investments they need to boost their business value in the short term and long term.
The labor landscape backdrop
Process intelligence may be particularly useful for insurers given the hiring challenges they have faced over the last two years. Like many sectors, insurance has taken a hit on the employment front in the pandemic.
“Since March 2020, overall employment levels in the U.S. insurance industry have remained flat, yet wages have increased on average 8%,” as we wrote in RSM’s
Insurers are feeling the impact of the talent war now more than ever. With the mass exodus of baby boomers from the workforce and fading labor growth (the labor market grew 1% annually on average from 1945 to 2005 but is now below 0.5%), there is a sense of urgency among such businesses to digitally transform their organizations. Insurers have been using technologies to fill the labor gaps in business processes that are ripe for automation.
Challenges in adoption
There are plenty of ways technology and data can help guide decisions and allow organizations to continuously improve and become more proactive. But due to fragmented technology applications, (i.e. applications that can’t digitally integrate), many insurance companies are hesitant to invest in
But digitalization will be critical to the survival of organizations through 2022 and beyond, whether they want to embrace it or not. To avoid the pitfalls of fragmented technology applications or lack of sustainable change, companies need to start with process mining their data to understand how the work is being done.
And that sustainable change may itself be the biggest transformation hurdle for insurers to achieve. Exponential digital improvements continue to outpace the insurance industry. If companies are constantly playing catch up, it’s hard to be proactive. However, process mining allows for analysis that isn’t antidotal or theoretical. Having access to such powerful information unlocks opportunities to make investment decisions that are based on what’s really happening. Coupled with continuous monitoring of process improvements, companies can begin to proactively respond to changing customer needs.
Finding gold
Process intelligence requires mining data and analyzing the data outputs to arm organizations with powerful, actionable information to determine whether efficiency gaps stem from people, process and/or systems issues. This helps manage business process execution more efficiently and pinpoint where to make transformation investments.
Insurers that want to be successful in harnessing process intelligence also need to remember that transformation of any kind requires continuous monitoring and adjustments to achieve returns on investment and sustainable change for the long term.
Once organizations understand patterns, loopholes and workflows that exist in their processes, they can identify hidden opportunities to improve service, quality and performance. Those who monetize operational data will be better equipped to understand what opportunities exist, make better decisions, reduce costs and serve clients in a quicker and more meaningful way.