Insurance

Motor Liability Decisioning

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AIS provide cognitive technology solutions to the UK motor insurance industry and are currently focused on helping insurers and third-party representatives make better accident liability decisions.

The problem

Identifying who was at fault in a motor accident (referred to as a liability decision) is a difficult task and an historical area of weakness in the motor insurance industry. There are a number of problems with the liability process which increase claims costs throughout the industry.

The cost of car insurance now gobbles up 10% of a young driver’s average salary. Correcting issues at the beginning of the claims process can reduce the costs of slow or incorrect liability decisions.

First Notification of Loss (FNOL) is the process where a customer first notifies their insurer. These calls are generally handled by contact centres where employee turnover is high. Contact centre roles are often low paid, employee engagement poor and handler error is common as a result.

A poor liability judgement at FNOL can cause claim delays and result in significantly higher costs being incurred by the insurer later in the claim process. Poor liability judgements can also impact an insurer’s reputation and customer satisfaction levels.

An incorrect liability decision may be costly, but even a slow liability judgement at the outset can affect the insurers ability to retain control of the claim.

Insurance fraud cases are expected to double with the rise of automation. Better liability decisions can spot fraudulent claims earlier in the process

While liability has been a significant area of friction in the industry for some time, Rainbird is the first technology recognised as capable of truly automating this process, providing a model of liability that is capable of delivering comprehensive complex judgements.

The solution

The Rainbird team sat down with the AIS liability experts to encode their best-practise methods into a knowledge map. Rainbird takes each claim’s data and compares it with the knowledge map, asking efficient questions via a simple chat interface when it needs more data. The result is an accurate automated judgement, using a probabilistic method modelled on human expertise.

“I don’t come from a development background at all – I’ve got no professional IT skills – but I’ve been able to use the platform to code the knowledge that we’ve got.” – Danny Thomas, Chief Solutions Architect at Automated Insurance Solutions

Rainbird handles any uncertainty and missing data and simply takes that into account when providing feedback.

“Existing liability solutions actually require a great deal of understanding from the users in order to get the best from them.” – Ben Taylor, CEO of Automated Insurance Solutions

Each outcome includes a rationale and a full audit trail backed by evidence, satisfying the client’s obligations to Treating Customers Fairly (TCF) and their other regulatory obligations.

By implementing the solution within a liability process, insurers and others in the motor claims industry see a material improvement in both the accuracy and consistency of front-end liability decision-making.

Based on current industry claim costs, even a reduction of 2% will yield savings of more than £40 per claim.

Become a truly intelligent automation and decision-making organisation
Find out how Rainbird can ensure every decision in your organisation benefits from the required expertise.

Intelligent fraud detection

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Rainbird was approached by an international credit card provider who processes over half a million transactions per minute.



The number of reported fraud cases in the financial sector nearly doubled in the last year, with businesses struggling to keep up with ever-shifting vectors of attack, from new modes of mobile fraud to skyrocketing rates of identity fraud.

They wanted to improve fraud detection, reduce the time it takes to deal with false positives and improve customer satisfaction.

The ISMG reported that only 34% of C-level leaders have high confidence in their organisation’s ability to detect and prevent fraud.

The challenge
The complicated fraud-detection process required an offshore team to continuously monitor data feeds on multiple screens to determine whether customer transactions flagged by their algorithms were fraudulent or genuine. This manual process led to erroneous judgements, resulting in many unnecessarily blocked cards and huge frustration for customers.

40% of cardholders abandon cards after false declines, and a quarter of these people move their cards to the back of their wallets

In 20% of cases, the offshore team phoned the customer directly to request additional information, which resulted in expensive security steps, language barriers, and, in some cases, lost customers.

The solution
We spent time with the company’s fraud-prevention Subject Matter Experts (SMEs) to create a Rainbird model that identifies fraud by replicating their best-practice methods, without relying on humans. This reduced the time spent on each case, and improved accuracy and consistent decision-making.

To do this, Rainbird took the logic from over 50 fraud cases, along with the expertise of three of the business’ best-performing SMEs to model a Rainbird-powered fraud engine that represented the company’s best-practice procedures, which was capable of handling multiple concurrent transactions simultaneously and consistently.

We also created a new model for prioritising cases that needed expert involvement so that the most high-risk cases can be assessed by the client’s most experienced on-shore team members.

Rainbird automatically calculates every transaction with a percentage likelihood of being genuine or fraud.

The outcome
The result of our work was an 85% automation of all cases and a 60% reduction in back-office processing costs.

The new process avoids the less accurate and inconsistent judgements that were being made by the client’s offshore team. 

The company benefits from:

  • Increased detection rates
  • A reduction in the number of false positives
  • Cost and inconvenience of outbound phone calls to customers

Calls to customers are now backed by Rainbird’s in-built Audit trail, which can explain why Rainbird has concluded a fraud risk and better inform the customer conversation leading to consistent, better-quality calls, and demonstrably better customer outcomes.

Become a truly intelligent automation and decision-making organisation
Find out how Rainbird can ensure every decision in your organisation benefits from the required expertise.

Human-centric automation can save insurance from a vicious cycle of ethical challenges

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For all the bluster in the sector, insurance and AI are still at a crossroads. Guidelines are being drawn out and full integration is pending. A recent C-suite poll from PwC found that 80% of global insurance chiefs believe AI is already integrated into their business or would be within the next three years, whereas a 2018 Capgemini survey revealed that only 2% of insurers worldwide have seen full-scale implementation, whereas 34% are still in ‘ideation’ and 13% use-case testing.

In other words, the future form of AI adoption in the insurance space is still up for grabs. Perfect timing, then, for the Centre for Data Ethics and Innovation (CDEI) to publish a paper on ethical AI in insurance, incorporating the Chartered Insurance Institute (CII)’s Digital Companion to their Code of Ethics. We examine this and more in the following list of ethical challenges facing the soon-to-be AI-powered insurance industry.

1. Corrupt claims optimisation

One of the more sinister guises AI in insurance could take is the practice of calculated claims optimisation – i.e paying out the minimum amount that a customer will accept without complaining. This could create a vicious cycle: if customers believe that insurers will not pay out the full claim, then there’s an incentive to inflate the size of the claim that is submitted.

Insurers need human oversight and full transparency to safeguard customers from such cost raising tactics – and protect themselves from inflated claims.

For claims optimisation done the right way, read our take on streamlining the FNOL claims process here.

2. “Uninsurable”? Unfair

The
CDEI has warned that while AI could cut fraud and better enable insurers to price risk, there is a danger that certain individuals – a new class of “uninsurables” – could be hit with unaffordably high premiums. “The price of insurance products could increase for some individuals to the point where they effectively become out of reach,” the paper claims.

Beyond the obvious ethical concerns, this could quickly become another vicious cycle in commercial terms. “Were price rises to affect a large number of people”, the report warns, “the customer bases of insurance companies could shrink to such an extent that risk pooling becomes impractical.”

A clear audit trail for each automated decision can eradicate this biased treatment. For more detail on the Rainbird in-built audit trail, visit our Platform page.

3. Fraud failure

There’s no polite way to put it: insurance companies aren’t doing enough to fight fraud, and their customers are taking a collective hit.

Deloitte estimate that annual fraud-related costs add up to 10 percent of insurers’ overall claims expenditure, while premiums are soaring across the space.

Whether due to specific regional or market trends or persistent areas of vulnerability, it’s important to remember that every insurance company’s fraud profile is unique; there isn’t a one-size-fits-all solution. A tailored approach to fraud prevention is key: this means investing in decision-making platforms that are configurable, scalable, and based on the logic of firms’ best gatekeepers of fraudulent claims.

“There’s no substitute for good old-fashioned claims and underwriting experience,” wrote one McKinsey senior partner.

Go more in-depth with Rainbird COO James Loft’s piece on insurance fraud here.

4. Knowledge leakage

The nous and expertise of seasoned decision-makers in detecting and examining insurance fraud will arguably be at a premium in years to come – particularly with waves of baby-boomers exiting the industry and less traditional workforce demographics replacing them.

Workforces are becoming more transient, more fluid; the odds of employees moving on for a different experience rather than gathering years of experience at your firm are rising all the time. Firms should be acting now to not only nurture and preserve their most valuable people, but also scale and maximise that expertise that they possess.

Find out how your best can capture their logic in Rainbird knowledge map.

5. Dodgy data

80% of the insurance executives surveyed for Accenture’s Tech Vision 2018 reported that their organizations increasingly use data to drive automated decision-making at scale – yet a recent study estimated that 97% of business decisions are made using data that the company’s own managers consider to be of unacceptable quality.

Society as a whole has yet to fully come to terms with the collection and usage of personal data to improve their experiences as consumers. Customers are divided on what they see as a valuable and ethical use of AI and data processing, and from an operational standpoint, any reliance on monumental amounts of digits introduces a new risk that Accenture calls “data veracity”.

Will the industry and its regulators need to establish new parameters around the required consent of customers before insurers begin drawing upon all kinds of personal data to make decisions? The use of big data could open the door to unprecedented levels of to “infer characteristics” about customers, as CII managing director Keith Richards puts it. The report highlights how one insurer draws on 1000 data points to judge the risk of someone making a motor insurance claim – including something as miniscule as whether they drink bottled or tap water.

6. Bad press

We’ve heard about risk exposure in insurance – but arguably just as damaging is the unwelcome media exposure when malpractice occurs.
In one such example, a mystery shopping investigation by The Sun newspaper found that insurers had given higher premium quotes to motorists with the name Mohammed.

Trust has been a huge issue in recent years for the insurance industry. Shady brokers or dodgy, unfair practises, such as slyly jacked-up premiums, have damaged the relationship between insurers and consumers and dented reputation at a PR level.

The best way to rebuild trust? Transparency. If claims handlers can keep their customers more thoroughly informed about claims decisions, with more detailed accounts of the rationale that was applied, customers can rest easier – even if the decision is an unwelcome one.

To achieve this while maintaining an efficient claims process, human-centric and transparent automation is really the only option firms can take.

Become a truly intelligent automation and decision-making organisation
Find out how Rainbird can ensure every decision in your organisation benefits from the required expertise.

81% of UK businesses say a shortage of talent is the biggest hurdle to AI adoption

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The research, conducted by Vanson Bourne on behalf of Rainbird, surveyed senior decision-makers in enterprise organisations. It found that the main reason behind businesses not implementing AI is a shortage of talent in their workforce for handling automation processes. While this was the overall biggest barrier to adopting AI in the UK, when broken down into professional services, financial services, insurance and IT, the data highlighted a number of different concerns across business functions.

Find the full report here

James Duez, CEO at Rainbird, commented: “In order to truly understand what processes will benefit from AI, businesses must review their strategies. Rather than pushing AI investment into IT departments, organisations should recognise where the most important decisions are being made – within the business. Symbolic tools are business-friendly, rapid to work with and completely auditable and it is these that will unlock the streamlining and automation of operational decisions.”

Become a truly intelligent automation and decision-making organisation
Find out how Rainbird can ensure every decision in your organisation benefits from the required expertise.

Neural networks can disempower human workers: the case for human intervention amidst rapid AI adoption

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Neural networks have become the alchemy of our age, the search for a magical, mystical process that allows you to turn a pile of data into gold. It is widely seen as a silver bullet that can generate new insights and expert decisions on an unprecedented speed and scale. Yet this ignores the reality that ‘deep learning’ systems are difficult to create or audit and most organisations lack the necessary in-house expertise or ‘data hygiene’ to use it effectively.

To read the full article, see Neural networks can disempower human workers: the case for human intervention amidst rapid AI adoption on Digitalisation World. 

 

 

 

Become a truly intelligent automation and decision-making organisation
Find out how Rainbird can ensure every decision in your organisation benefits from the required expertise.

Ghosts in the Machine: How Machine Learning is Transforming Business

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According to new research from Rainbird – the AI-powered automated decision-making platform, 81% of those surveyed revealed that their organisation planned to increase investment in AI over the next five years. Of those who plan to increase spending on automation technologies, 22% suggested this investment would be significant. Interestingly, the financial sector is set to be the biggest adopter, with 94% of those surveyed planning to increase investment in AI over the coming years.

James Duez, CEO at Rainbird, commented: “AI should be brought into organisations to help employees, not hinder them. UK organisations – and beyond – need to fundamentally change the way they are adopting AI and, think beyond big data and machine learning. ‘Data scientists only understand black box solutions, and there are huge benefits to be had by moving towards more transparent symbolic technologies which can achieve automation outcomes beyond those available with data-only approaches. Such accessible tools also have the added benefit of addressing the skills gap by making AI far more accessible to employees without a degree in data science.”

To read the full article, see Ghosts in the Machine: How Machine Learning is Transforming Business on Silicon UK.

Become a truly intelligent automation and decision-making organisation
Find out how Rainbird can ensure every decision in your organisation benefits from the required expertise.

91% of UK IT businesses say a shortage of talent is the biggest hurdle to AI adoption

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The adoption of Artificial Intelligence (AI) and automation technologies in the UK is still being stunted by a lack of digital skills in businesses according to new research by Rainbird, the AI-powered automated decision-making platform.

To read the full article, see 91% of UK IT businesses say a shortage of talent is the biggest hurdle to AI adoption on Digitalisation World.

Become a truly intelligent automation and decision-making organisation
Find out how Rainbird can ensure every decision in your organisation benefits from the required expertise.

How AI can simplify mergers and acquisitions

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CTO of Rainbird, Ben Taylor, is featured in IT Pro on the importance of auditability in automated M&A decisions:

“If a financial institution gave incorrect advice that led to a failed merger, it might be difficult to tell how the neural network got the decision wrong.”

Read the full article, How AI can simplify mergers and acquisitions, on IT Pro.

Become a truly intelligent automation and decision-making organisation
Find out how Rainbird can ensure every decision in your organisation benefits from the required expertise.

First impressions are everything at FNOL

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At First Notification of Loss (FNOL) in the insurance claims process, the consequences of a bad first impression are rather more quantifiable. More than 30% of customers who endure a bad claims experience switch insurers within a year of the incident, according to Forrester.

Lost customers are a hefty price to pay for a bad day at the office. Luckily, the unwavering consistency of automation allows us to safeguard ourselves against those bad days we’re all prone to. 

Scale human expertise

A common problem at FNOL is inconsistency in the application of standards across a business. Put simply, some know more than others – particularly in the high-churn field of insurance claims. 

According to a 2019 PWC study, it’s common for new staff to start processing claims after only a couple of weeks of unstructured training without sufficient access to expert knowledge, leading to mistakes, job dissatisfaction and high staff turnover, which in turn makes it difficult to keep expertise within the business.

On top of this, any changes to legislation or the processing of claims means a time-consuming re-training process and more room for potential errors.

With resources stretched, employee turnover high, and frequently changing legislation, not every claims handler can be an expert in their given field – be that collision damage or work-related injury. But with a Rainbird map encoded with the best-practise methods of their company at their fingertips, each employee can access and apply the expertise of their most experienced and specialist colleagues.

Free up your team

Establishing this quality baseline for best-practise means that a department can shorten its cycle, and focus more of its energies on going the extra mile for its customers – and getting that first impression spot on every time.

An AI-enabled claims department means claims adjusters can spend 95% of their time optimising indemnity and customer service, according to Genpact. A recent EY study meanwhile found that 87% of policyholders reported that the initial claims experience significantly impacts their decision to remain with their provider. It doesn’t take a rocket scientist to do the maths here: if you nail the accuracy and efficiency of first interactions at FNOL, your customers will more than likely stick around.

Become a truly intelligent automation and decision-making organisation
Find out how Rainbird can ensure every decision in your organisation benefits from the required expertise.

UK customers are famously good at queueing; that doesn’t mean we enjoy it

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Peek inside a modern contact centre, and you’ll usually still see a segmented setup; one department for account review, one for security checks, and so on.

This siloed structure is counterproductive and only lengthens waiting times for customers on the line. With siloed lines of banking onboarding, for instance, customers may wonder why they’re having to duplicate the same information for different officers. Or there may be a long line of customers inexplicably on hold at the security check stage, where there happens to be a bottleneck buildup of cases waiting. 

Breaking this linear structure with cross-functional models is the solution. An integrated ticketing system – whereby each station has access to expert knowledge wherever it’s needed – means customers can be fast-tracked out of their excruciating queues. True, we may be renowned in the UK for our queueing etiquette – but that doesn’t mean we like it.


Smooth operators

Platform technology, rather than siloed software, is what can gift businesses this knowledge sharing and operational alignment across divisions, so that each station can handle tasks wherever demand is highest – whether in account review, security checks or new account enquiries.

In the outdated model, customers who select “option 3” could be done and dusted while customers who select “option 4” are listening to a loop of Mozart’s Symphony No.4. But with a platform of centralised expert knowledge made accessible across each call centre department, the Mozart-on-hold music can be canned altogether.

If the customer experience is “the new battleground”, as is so often acknowledged, then call centre agents are the troops on the frontline – and they need to be properly armed with the right information, at the right time. Providing best-in-class customer service while keeping operational costs low is a big ask. But when call centre managers fail to fully utilise quantitative models or workflow management to optimise their operation, it becomes an impossible ask.


We still need a human touch

Many have trialled automated online messaging systems, but with mixed results. Consumers love the convenience of messaging, but struggle with its experience silo, according to a study conducted by YouGov on behalf of eGain, based on responses from 1,777 consumers in the US and UK.

43% hated repeating information across agents or when escalating to other channels; 28% struggled with the speed of response; and 23% were stumped by the relevance of response.

Ultimately, a well-informed and timely assistant with a human touch, rather than underdeveloped chatbots, remains an unbeatable formula.


Unite competing truths with centralised knowledge and cross-functional workflows

Too often, call centre agents work in silos with a criminal lack of communication across departments, and end up either repeating or – even worse – contradicting each other. This is especially troublesome when a wrong decision early on impacts the rest of the customer journey and leads to increased operational costs. For example, decisions on whether an agent should work a file – which incurs additional costs – or wait for other parties to provide additional information are often made on a merely arbitrary basis.

These are decisions that should be backed-up by best-practise company logic, which employees can’t all be expected to possess – particularly when churn rates are high. Like binding glue, cross-functional, integrated workflows and centralised knowledge can turn a siloed call centre into an agile and cohesive operation. Most importantly of all, customers can begin to say goodbye to the phrase: we are experiencing higher than normal call volumes”.

Become a truly intelligent automation and decision-making organisation
Find out how Rainbird can ensure every decision in your organisation benefits from the required expertise.