Successful RPA implementation to increase productivity up to 30% in the Insurance sector

Robotic process automation (RPA) technology is embraced by enterprises globally for varied sectors. The global robotic process automation market is growing in size with a CAGR of 24.9% between 2020 to 2027 and is expected to touch USD 6.10 billion by 2027.

The insurance industry is still exploring ways to leverage its capabilities to their full potential, those who have managed to make inroads are already experiencing extraordinary benefits. Considering that insurance is now an integral part of our lives, the benefits received from RPA adoption are enjoyed by insurers and customers. As a heavily regulated sector, insurance comes with a lot of documentation and standardized processes. Be it minimizing errors in claim processing or speeding up fraud detection processes, there’s a lot RPA can do to empower the insurance sector.

In its 21st CEO Survey, PwC iterates the importance of organizations becoming ‘bionic’ to enable humans and machines to work together to blend emotional and technological capabilities. It further repeats the role of RPA in making them bionic, citing it as the key to this transition.

Each product line in the insurance industry varies in its degree of standardization. Needless to say, the potential for automation differs too. Yet, experts unanimously agree on the incredible benefits of RPA in helping insurance companies save time and money across critical insurance processes.

According to Mckinsey, RPA promises a 30-200% return on investment in the first year.

Current insurers are now combining RPA with Artificial Intelligence (AI) to handle bigger workloads and analyze large volumes of data to convert them into actionable insights for better decision-making and business outcomes.

As you move forward with your RPA strategy, here are a few examples on how it can help increase productivity and reducing costs.

Underwriting & Pricing

According to Accenture, an underwriter typically spends more than 50% of the day on core processing. The market for technologies facilitating underwriting improvements has been growing continuously, and it comes as no surprise that RPA and AI are providing a huge impetus. RPA helps in ensuring accurate data checks to understand the insurance history of customers.

A significant part of underwriting involves determining the right price taking into consideration the risks involved. Insurers depend on RPA, AI, and analytics to get information from connected devices, sensors, and wearables. This data in real-time provides useful insights into customer-specific risks while providing an opportunity for insurers to offer personalized services and customized covers.

The services they offer also include alerts, rewards, and messages that help them transcend their roles from being risk insurers to risk mitigators. RPA can also assess loss runs and provide pricing options based on customers’ claims insurance history.

Global insurer Zurich, used RPA to free up its commercial underwriters to devote their time to more complex policies while boilerplate policies get handled by smart software.

Sales and Marketing

RPA helps insurers address the ‘advice gap’ from online portals that offer price and feature comparisons. Customers can now chat with their virtual insurance manager, convey their needs, and get the right advice and recommendations. RPA also enables insurers to mine non-traditional data that comes from social media, allowing them to upsell and cross-sell based on individual activities. For instance, those planning to travel may want additional coverage if they are going to participate in activities such as paragliding or skiing.

RPA helps agents find the information they need without having to wait for a sales representative to assist them. It also helps companies manage their brand persona online while ensuring compliance with rules and regulations.

Customer Service

A survey by TechSee confirms 39% of participants admitted to having canceled their contracts due to poor customer service. But with the foray of RPA, back-end processes are now linked to front-end service so perfectly that chatbots now identify the customer even before a call is answered.

Customer-facing chatbots can help customers with everything they need right from providing policy status and payment details to sending automated policy renewals and pre-loss warnings. RPA also takes care of reporting and compliance requirements so that records are readily available for quick reviews, reconciliations, and compliance checks.
UK based leading insurance carrier, Swinton implemented RPA technology to provide agents with assistance in handling new processes and to guide them through customer journeys. This shortened calls by over 50 seconds each on average and the contact center capacity was increased to handle an extra 7,781 calls per month.

Claims and Fraud Detection

TechSee points out how customers absolutely expect 3-second claim payouts and will not think twice before taking their business elsewhere if their expectations are not met. This is where RPA comes in. It offers the much-needed speed and accuracy required in a claims journey and self-service options to induce transparency into the process further.

Digital interfaces are convenient and preferred, too, as insurers introduce novel ways to settle claims. Apps are also being used to enable customers to assess damage to their car in case of accidents with their smartphone cameras’ help. The apps offer repair cost estimates based on thousands of images they have been trained on. RPA also helps in fraud detection and identification of suspicious claims with predictive analytics and machine learning.

Policy Management and Regulatory Compliance

While it may be expensive to have a dedicated policy administration software, RPA comes as a simple, safe, and scalable alternative to manage diverse policy activities. These include accounting, settlements, risk capture, credit control, regulatory compliance, etc., that need to be accurate but come with a high risk of errors if done manually.

US insurer Lemonade set a world record in paying a claim within 3 seconds of receiving details. The customers can explain the situation via their phone camera and submit it without paperwork. The Lemonade claim bot then runs algorithms on the details to validate any fraud. Post that payouts are instant.

RPA has proved to be a game-changer for the insurance industry, especially during the pandemic when the entire world has been forced to go digital. As Cliff Justice, a Principal in KPMG’s Innovation and Enterprise Solutions team rightly points out, “At this point in time, it’s not a question of whether you should or should not adopt RPA. You risk remaining relevant if you don’t. The question should be: ‘What’s the best way for your firm to employ RPA and AI.”

Automate Your Business with Trigent

In an ever-evolving world of innovations, we help you tide over diverse business challenges with automation solutions like RPA. Our extensive experience in the insurance sector gives us an edge in helping you transform your business. We will help you attain operational excellence while ensuring a faster ROI, all with the power of RPA.

Allow us to assess your business environment and tell you why RPA is the perfect fit for your business. Call us today.

Discover how predictive analytics is helping insurance companies minimize risk and fraudulent claims

As new technologies forge their way into diverse sectors, it comes as no surprise that the insurance sector is leveraging them for different reasons. Predictive analytics has found an important place in the insurance landscape for its ability to make data-based predictions.

The impact of predictive analytics in insurance sector

While predictive analytics helps the insurance industry gain great insights into customer activity and behavior, it also plays a massive role in preventing fraudulent claims and minimizing risks.

As per the Insurance Fraud Bureau, there has been one insurance scam every minute during the U.K.’s pandemic. Things are equally bad in the United States, where insurance fraud doubled to $100 billion last year.

The insurance industry is now moving quickly to mine data and track new rackets quickly. Explains Zurich’s head of claims fraud Scott Clayton, “By deploying the proper analytical tools, you can extract and interrogate the data, and use algorithms to highlight these links. By joining all the dots, you can soon identify persistent and prolific offenders.”

The pandemic’s unprecedented nature has set the tone for intelligent business practices that can shield them from fraud and help them strike back. Thankfully, predictive analytics, in tandem with big data, have answers to most of the problems insurers face.

How predictive analytics in insurance is minimizing fraud and risks

Understanding predictive analytics

To determine its role, we need first to understand that predictive analytics is an analytical tool that studies historical data to predict upcoming events and ensure business practices’ effectiveness. It gives organizations a competitive advantage and helps them stay abreast of changing trends. It looks into the data collected from different communication channels to analyze client interactions, agent feedback, customer behavior, etc., to build a more intelligent, data-driven ecosystem for all.

It’s no secret that insight-driven insurers are always better positioned to strengthen their capabilities in all five areas, namely, people, process, data, technology, and strategy. Predictive analytics helps them excel on all these fronts. 67% of those who recently participated in a Willis Towers Watson survey reported a reduction in expenses and a 60% increase in sales due to predictive analytics. Most importantly, it helps prevent insurance fraud.

The role of predictive analytics from a fraud prevention perspective

Insurance fraud has a significant bearing on the entire business, specifically on underwriting, and also causes a negative social impact. While undetected frauds drain finances and lead to many more scams in the future, those detected damage market reputation, and trust. Not to mention the legal issues that arise from them and the subsequent impact on future policies, procedures, and guidelines.

Predictive analytics helps insurers in the following areas to prevent fraud:

  • Pricing and risk mitigation – Offer insights that facilitate decision-making and estimate the level of risk that the insurance company has to assume while calculating the premium. For instance, those who go to the gym regularly may be eligible for a discount on health insurance.
  • Trends tracking – Helps insurers create new products, design new customer experiences, and deploy new technologies by keeping an eye on what’s trending in the world of insurance. This also gives insurance companies a competitive edge.
  • Fraud prevention – Helps insurers prevent fraud at different levels of the insurance cycle, including application, premiums, claims, etc. It offers a sneak-peek into public records such as criminal records, medical history, and bankruptcy declarations to review data for detecting inconsistencies and preventing frauds.

Dealing with insurance fraud

What’s frustrating is the fact that insurance frauds today are highly organized and occur digitally. Insurance companies have realized that the only way to fight and prevent insurance fraud is through data mining, analytics, and algorithms based on patterns in fraudster behavior.

Digital algorithms that have been hugely helping in timely scam detection are based on data pertaining to:

  • Referral history – Experts have created algorithmic models to estimate the probability of a claim going beyond a threshold level referred to Special Investigation Units or SIUs. This model typically uses the historical claims data referred to as the SIU to determine the probability value. Investigation scores are then calculated using investigation scoring automation techniques to distinguish between good risk and bad risk claims.
  • Historically rejected claims records – Based on the belief that claims that have been historically rejected stand a greater chance of being denied for doubtful potential frauds, digital algorithms automatically scan through the claims using several parameters. Claim Risk Indicators such as a customer’s SSN, address, contact number, etc., are carefully scrutinized using clustering-based data mining techniques. Claims are then categorized as ‘clusters with high claim frequency’, specifying the level of risk.
  • Individuals/groups – Digital algorithms, in this case, are based on data about individuals or groups that make fraudulent claims repeatedly. Flags are triggered every time fraudulent entities are detected, and these flags help identify fraudulent patterns.
  • Social media profiles – Algorithms, in this case, take into account social media profiles and interaction patterns of individuals along with other details such as lifestyle, attitude, etc. It takes into account mismatches between actual profiles of individuals on social media versus their claims. For instance, if an individual has forwarded an accident claim but their social media shows them partying with friends, there is certainly a mismatch that needs to be investigated. Algorithms based on social media posts are also useful in demarcating networks or groups of fraudsters.

Hurdles on the way to insure the future

Fraud detection is no longer static, limited to place or time. Every time a new detail is added, insurers now run predictive analytics at multiple touchpoints to enhance their fraud detection capabilities. Their efforts are now proactive instead of reactive and a great deal of effort is being put into fruitful collaborations with brokers and third-party vendors to build clear channels of communication and information exchange. But the quality of data still remains a big challenge.

Going forward, the focus should be on reducing data volumes and increasing data quality while ensuring that it is readily available as needed. The funnel needs to be narrowed in a way that competent individuals carefully review the results from machine analytics.

There are legislative barriers, too, concerning data sharing and individual privacy that sometimes stand in the way of data collection. Predictive analytics, however, is helping insurers make the best of what they have by sifting through information pools to help them produce intelligent products for the future.

Empower your insurance business with Trigent

Embark on a whole new journey with Trigent with predictive analytics at the helm. We can help you redefine your strategies to enhance risk management and ensure your future. Our disruptive suite of tools and solutions can transform your insurance business into a data-driven, efficient, and secure ecosystem.

Book a business consultation to know how we can help you keep insurance fraud at bay. Call us today.