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ARTIFICIAL INTELLIGENCE – IS THIS THE INSURANCE INDUSTRY’S LONG-AWAITED SAVIOUR?

Asheesh Mehra, Group CEO & Co-Founder at AntWorks

 

Artificial Intelligence and Machine Learning have gained somewhat of a reputation as a silver bullet for the insurance industry. This is mainly due to its capability of automating and delivering greater levels of intelligence to specific insurance and business processes. What’s more, with the help of AI, insurance companies can improve the overall efficiency of their operations.

Insurance companies adopting AI to improve their market standing gain competitive advantage as a whole. However, this was not always the case. The insurance industry has for a long time now been held back by its reluctance to innovate and adapt due to barriers of regulation, lengthy processes and product complexity. This could all soon change. Insurers are looking towards AI in a bid to provide faster, better, and more efficient services to their customers. This also coincides with the rise of Insurtech, the use of technology innovation in insurance. This means that insurance companies are now looking to move away from the traditional legacy-based modus operandi and adapting to an evolving marketplace. A marketplace where AI such as Intelligent Automation (IA) is infiltrating systems and transforming business processes.

 

A new opportunity

The insurance industry is one of the most regulated and oldest business service industries in the world. An industry built on trust and customer care that is still reliant on the human touch by way of complicated, manual, and paper-based processes. This may explain the industry’s lack of adaptability and its reluctance to embrace digital innovation.

Asheesh Mehra

As a result of this, customer care, one of the main pillars that the industry has been built on, lacked much-needed quality for a long time now. Customers have to endure time-consuming bureaucratic processes that are often not even tailored to their special needs or demands when it comes to applying for insurance policies. What’s more, the slow-paced nature of processes means delays in generating insurance renewals quotes will start to impact a firm’s ability to generate revenue. The sheer volume of business data, mainly comprised of unstructured data that needs to be processed and analysed, along with the complex business rules that need to be followed, point towards a desperate need for improvement. Time has now come for insurance companies to look at ways to improve their operational processes through technology innovation. AI has the capabilities to help insurance practitioners to do business much faster, more efficiently, and with greater security.

 

Insuretech: AI is here to stay

Many insurance companies have started adopting and implementing various different AI technologies in their operational processes in a bid to not only improve their customer experience but also their bottom line. One of them is Intelligent Automation (IA), the holistic automation of business data of all kinds. Many Insurers are looking to expedite renewal policy quotes with automation to gain faster and more customer centric results. IA can process large volumes of data and ensure the generation of faster, and more accurate results. In fact, according to a recent McKinsey report, automation can reduce the cost of insurance claims journeys by almost 30 percent. A leading HR consulting firm used IA to improve its processing speed by 70%. The company implemented the technology to manage large volumes of data and provide optimised quotations to customers for new policies and renewals. This in turn lead to higher customer renewals, an increase in customer acquisition rates and increased revenues.

This isn’t a surprise considering Intelligent Automation Platforms can analyse and process large amounts of unstructured data and complex business contracts at a fraction of the time compared to traditional insurance processes. IA does this two-fold; firstly by process discovery, as this can help identify and confirm the feasibility of process automation opportunities, therefore optimising repetitive human-driven processes and achieving enterprise-wide scalability. Secondly, by way of identifying and seeking out clean, readily available data via straight-through processing.

IA can also effectively eliminate the chances of delays occurring that could impact a firm’s revenue generation. IA platforms built with powerful data ingestion modules can read printed and handwritten text scanned documents and images, signatures, and PDFs. This enables insurers to generate more insurance renewal quotes in less time and with better quality. Faster, superior, and more tailor-made insurance renewal quotes will certainly improve the bottom line as well as customer retention and acquisition rates. In addition to this, enterprises will also be able to speed up workflows and unlock new revenue streams.

A benefactor of a national insurance provider used IA to abandon their pre-existing lengthy processing period for detailed documents. The technology managed to help them eliminate manual tasks by 75% and increase the processing accuracy.  More specifically, the IA platform not only helped process unstructured data that included handwritten fields, but also signatures and checkboxes within the policy documents. Crucially, integrated automation platforms can provide customers with a level of transparency not seen before, making it easier for customers to interact with brokers and review insurance rates. Naturally, this level of customer experience will likely lead to greater customer satisfaction and client retention.

For those companies in the insurance industry yet to explore the potential of AI, it is now time to consider how they can go about investing in the technology. Identifying the specific needs for automation and implementing the right AI tools will enable insurance companies to achieve far greater operational efficiency and stronger revenue streams than ever before.

 

Finance

AI: CUSTOMER FACING EMPLOYEES’ BEST FRIEND IN THE FINANCIAL SERVICES INDUSTRY

By Ryan Lester, Senior Director, Customer Experience Technologies at LogMeIn

 

We’ve all heard the old saying “money talks.” Well when it comes to customer loyalty and retention, good customer experience talks much louder, with 30% of customers leaving a brand and never returning due to a bad experience.

The truth is, there are a lot of companies with similar products and services, but that doesn’t mean that differentiation is impossible. So, what’s the solution? For financial services, large and small, customer experience is becoming the key competitive differentiator and the best way to deliver an impactful experience is to empower customer-facing employees to do their best work. Artificial intelligence (AI) is enabling these employees to create remarkably better customer experiences, resulting in customer loyalty, advocacy, and overall growth.

For financial institutions that have been considering new strategies for improving the quality and efficiency of their customer experience, here are a few ways AI can enable them to deliver the “human factor” that good customer experience demands whilst ensuring customer facing employees can provide a more positive experience for customers.

 

Increase employee productivity

How much of employees’ time is spent searching for answers to questions? Do they ever have to put customers on hold or even step away to get additional help? AI helps provide front-line employees real-time guidance so they can spend less time looking for information and more time solving problems. An AI-powered chatbot, for example, can be listening in the background of a conversation helping point employees to the right data, solutions, and processes to resolve customer issues faster than ever before.

 

Deliver a consistent customer experience

When banking customers engage with their financial institutions, they measure the speed and accuracy of the service through two criteria. First, how quickly can the system access their account and deliver the correct information? Is it faster than a human could type it in and share it? And second, if they eventually do need to be connected to a live customer support agent, is their information captured and passed along accurately? AI technology takes those general queries off the customer support team’s plate, providing a quick, accurate, and effective response. If a query needs a more in-depth response, AI can hand it off to support staff to address.

Not only this but leveraging a centralised, AI-powered knowledge solution ensures every employee has access to the same, updated information, so no matter who the customer speaks to, they can be assured that employee responses are both consistent and accurate across the board.

 

Accelerating employee training and onboarding

Like any industry, employee turnover is inevitable and can be costly. But, not training new employees correctly or in a timely manner could be much more costly. When it comes to financial services there is a lot to learn, whether it is something simple like the process for checking an account balance to all the nuances associated with mortgage loans. AI can support on-the-job training by helping new employees answer questions confidently, correctly, and much quicker than they could before.

 

Improving employee satisfaction

Today’s banking customer has all kinds of new ideas about their banking experience. “The Amazon Effect” has successfully raised consumer expectations to the extent that a consistent, personal, and relevant experience is the new normal. As a customer, how many times have you been told “I’m sorry, I don’t know the answer?” Customers want solutions to their problems and employees want to be able to deliver those solutions as efficiently and effectively as possible. AI assisting in the background helps minimise those negative moments – making employees job easier, less stressful, and overall more enjoyable.

 

Identify knowledge gaps

Do you know all the questions employees are getting asked? Do you know what’s easily answered and what’s not? Real-time insights allow knowledge managers to keep up to date on frequently asked questions and gaps in current resources. This allows them to strategically improve or add content where needed.

 

Augmenting customer service

Whether talking with an AI chatbot or a personable customer service team member, the modern banking customer has high expectations for convenience, speed, and security. Which means that the technology you choose to deploy and how you deploy it is now just as important as who you hire and how you train them.

Today’s AI solutions won’t replace customer service agents or get in the way of the human factors that drive the customer experience. On the contrary, they augment it, allowing the business to do more without adding human resources. The higher the quality of a AI chatbot solution, the better it will be at taking the routine requests off the plate of customer service agents—giving them more time to provide a personalized and positive experience for customers.

 

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Technology

BEFORE THE INK IS DRY: CORRECTING BIOMETRIC SPOOFING MYTHS

Eric Setterberg, System Design Engineer at Fingerprints

Biometric authentication is highly robust, and the latest solutions offer considerably greater security than their authentication predecessors: PINs and passwords.

But as biometrics moves into new areas such as payments and access control, privacy and security concerns are rising. Biometrics has long been subject to scrutiny, with many elaborate examples of people working to trick biometric sensors to crack devices in the media and online.

To ensure the continued adoption of biometrics, it is important to shine a light on the reality of biometric spoofing.

 

The Evolution of Biometric Solutions…

The first use of fingerprints as forensic evidence was in an Argentinean court case in the late 1800s. With the technology still in its infancy, this was done manually and by eye, comparing latent residual prints lifted from crime scenes to charts of inked fingerprints obtained from the suspects at arrest.

A few decades later, the FBI began collecting fingerprints of criminals and civilians. They also introduced the automated comparison of fingerprints by computers in the 1970s. These “traditional representations” have now been standardized by ISO and ANSI.

… and their Spoofs

The earliest and simplest of these matching devices were easy to spoof. Really, all you needed was a photocopy or a good image of a fingerprint to make a successful spoof.

But as biometrics moved to more advanced technology, the game for biometric ‘spoofers’ has changed and the task of crafting fake fingerprints is considerably more difficult.

The biggest boost for biometric security, however, came with its introduction into mobile phones.

 

How Mobile Changed the Game

Before the widespread integration of fingerprint sensors in smartphones, the technology underwent significant evolution. No operator wanted to use large biometric sensors in modern phone designs. Sensors had to become much smaller to reach the perfect price and design point for the mobile world, but this meant needing to capture data from a smaller surface area of the finger.

To maintain the security of these smaller sensors, algorithms evolved significantly in order to utilize a greater amount of data per unit area. These mobile-driven hardware and software changes resulted in the optimized image capture of modern touch sensors.

As a result, tricking these systems now requires a considerably higher level of detail to be reproduced correctly for a match to be successful, far beyond rudimentary gummi bear spoofs and photocopies

 

Setting the Perfect Spoofing Scenario

Compromising fingerprint authentication via spoofing can still be done, even with all the technological advancements. However, it now requires considerable care, skill, money, and time. And to start, a good latent print…

To retrieve a latent print that’s high quality enough to work, you either need a willing volunteer to lend you their finger, or the commitment to stalk a victim until a viable fingerprint can be retrieved. Even with a decent latent print, modern spoofs then require advanced photoshop skills and/or a lab to successfully convert latent prints into effective moulds.

So – what about those articles boasting how easily they have hacked the latest smartphone device’s fingerprint sensor?

In fact, there are only two instances of fingerprint spoofing seen in the media nowadays: proof of concept and cooperative spoofs. Lay enthusiasts and media go through the effort of setting up a lab to create spoofs with latent fingerprints either from themselves or cooperative volunteers. Even the most successful of these take months of work, a highly skilled team, and the perfect scenario of circumstances.

Put simply, the effort required for spoofing modern fingerprint sensors cannot be applied at any scale. Each biometric spoof needs to go through the same laborious process and clinical conditions. So, if you can bring together a willing group of spoofing enthusiasts, tricking a biometric device could earn you fifteen minutes of fame on the internet, but it is likely to be conducive to a successful criminal business plan…

 

A “How” Without a “Why”

Spoofing biometrics remains technically possible, and there will always be those up to the challenge of trying to hack the latest technology. But the reality is that modern biometric solutions require more time, skill, and frankly, luck, to successfully spoof than ever before. Not to mention that tireless R&D work is continuously strengthening spoofing resistance. And, as use cases start to combine multiple biometric authenticators, such as combining fingerprints with face or iris to perform an authentication, spoofing will only become more complex.

By comparison, hacking PINs and passwords is considerably simpler and more scalable, making it far more lucrative. And, criminals generally take the path of least resistance.

For the average consumer, greater use of biometric authentication is not only a means of simplifying authentication, but dramatically improving the security of their devices, applications, and personal data. With PINs and passwords still the most common authentication method outside of mobile, it is imperative that the true security and advanced nature of modern biometric authentication solutions are understood.

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