By Alan O’Loughlin, Director of Analytics and Statistical Modelling, International and John Beal, Senior Vice President of Analytics at LexisNexis® Risk Solutions
The Insurance industry has been dealing with vast volumes of data for years, but analytics, Artificial Intelligence (AI) and Machine Learning (ML) techniques are increasingly being used to help insurance providers make faster data driven decisions. Given the exponential level of data available today with AI/ML, insurance providers can now efficiently extract new insights into their customer’s needs and create stronger long-term value.
Personalising Insurance Pricing
Starting with how the market calculates premiums, the insurance sector now has access to thousands of data points to help them calculate premiums. Machine learning algorithms expedite the identification of the most predictive attributes driving claims losses – the most recent data points being historical cancellation data and gaps in cover.
This helps insurers become more competitive, match their risks to the most appropriate pricing strategies and write the risks that meet their underwriting appetite. In turn, customers get more personalised quotes based on their unique risk characteristics across any line of business
Achieving a single customer view
Personalisation within any sector works best of course when you really know who you are dealing with. Today, an explosive amount of data is collected, but it is vastly under-utilised as many organisations do not have the expertise to bring data together from different parts of the business to create a single customer view. Add to this, the amount of mergers and acquisitions in the insurance market over the past few years and the challenge of managing multiple customer databases. Linking and matching technology using policy history data to find common threads helps overcome this problem to create one consolidated view of the customer. Optimised matching algorithms are also the most accurate and relevant data is reviewed, reducing consumer friction during the quoting process.
Normalisation makes sense of masses of data
In the same vein, as organisations aggregate massive volumes of data, the value of cleansing and normalisation can’t be overlooked. One example, as usage-based insurance develops, whether through aftermarket telematics devices, smartphone apps, connected vehicles, even in the future from smart home data, all that data needs to be gathered, normalised, standardised. That way, any consumer can enjoy an improved shopping experience based on their needs and preferences, no matter the device brand and insurers have consistent quality standards and outcome decisions for all consumers.
Making Vehicle Data work for insurance
Data normalisation is already helping insurance providers understand the presence of Advanced Driver Assistance Systems (ADAS) on a vehicle at the quotation stage. An ADAS classification system has been created using machine learning to scan millions of lines of car manufacturer vehicle data to logically sequence and classify vehicle safety features and component’s intended operation or purpose. Extraction and proper classification of this type of data is extremely difficult, time consuming and error prone without the use of AI/ML
Thinking big, starting small in motor claims
At the claims stage in motor insurance, image recognition technology is being used to capture damage or invoices, run a system audit, and if the claim meets the approved criteria, it is automatically paid without human involvement. This kind of virtual or ‘touchless’ claims handling is speeding up claim settlement times, cutting costs and improving the customer experience. The ability to quickly analyse years of historical policy and quote history at the consumer level will add an additional level of security prior to a carrier releasing any claim payments.
Building context through AI and ML
Staying with motor insurance, telematics data can be used much more broadly than originally intended through AI and ML. From the point of impact through to claim resolution, telematics data can allow insurance providers to get on the front foot at first notification of loss (FNOL), helping to deliver a better consumer experience post-accident, whilst providing invaluable insights regarding the circumstances of the collision.
AI/ML techniques communicate the conditions before, during and after the time of the accident. Data points like air bag deployment impact sensor activation and g-force metrics can be analysed to understand claim severity and bodily injury potential. In addition, by combining vehicle build data, carriers can understand the repair cost and potential impact to expensive ADAS features. Insurance providers can instantly also help their customers with emergency services, vehicle rentals and repairs through instant analysis.
Taking the pain from home insurance applications
Moving into home insurance, we know that conversion rates of people shopping for home insurance is quite low due to a number of hard to answer questions along the customer journey. Rebuild costs is a classic example. Prefill and data validation solutions are now helping to solve that problem but they are only possible through a huge amount of modelling, linking and AI-ML techniques to pull all the data together to return accurate and up-to-date information on the person and property.
Putting customers in the picture
AI is also at work in the commercial property insurance arena. It can provide valuable insights regarding a potential location for a new branch or business relocation – footfall, crime rate, exposure to perils or other local circumstances that increase risk. This insight when provided to the customer enables them to take preventative measures if they do go ahead in that location, decreasing risk and loss costs, whilst helping to improve customer experience and retention.
AI and ML can help in the democratisation of data
Finally, AI and ML techniques are helping consumers take advantage of their individual data points which in turn provide the most accurate and updated view data to the insurance providers they choose to interact with on their own schedule.
A good example is the way driving behaviour data from aftermarket devices, or in the future, direct from the connected car gives a clearer picture of someone’s driving risk on the road. Drivers then benefit from being judged based on their individual behaviours, rather than paying premiums based on average driving habits.
This requires transparency. Each time a consumer applies for insurance they consent to their data being used to provide the insurer with the best information possible, so they can set an appropriate premium based on the risk. Within insurance, we are focusing more than ever on educating consumers about how their data can be used and evaluated in a way they control and understand. AI and ML automate and process the data consumers are happy to share – supporting greater choice, improved fairness and reduced friction with more personalised insurance protection.
Everything you need to know about APIs for business
By Omar Javaid, president, Vonage API Platform, Vonage
If your work brings you into close proximity with technology, chances are that you’ve come across APIs. Like many of the tech acronyms we hear – DNS, VOIP, SaaS – APIs fall into a category of terms that most of us would consider best left to the IT department. However, APIs are a vital tool for any tech-enabled business, and a basic understanding of them at management level can help to drive sales, increase customer satisfaction, and improve the user experience.
Although they seem daunting, getting to grips with APIs is surprisingly straightforward. API stands for Application Programming Interface, and can be simply defined as a software tool used to control programmes. Essentially, APIs create sets of rules that allow applications to communicate with each other – they are the part of the server that receives requests and sends responses. Today, when data is transferred between a pair (or more) of programs or applications, an API normally makes it happen.
To give a real-world example: when a user types Instagram’s URL into their browser and hits the Return key, a request is subsequently transmitted to Instagram’s remote servers. That browser then processes the response code it receives and displays the page. For the browser, Instagram’s server is an API – allowing it to communicate and relay information back to you without interruption or delay.
The job of the API is to simplify the complex data exchanged between these servers, and to make the interaction as seamless as possible for the end user. Considering that the vast majority of our business and personal lives now take place virtually, any solution that optimises the online experience is extremely valuable.
Using APIs to improve the customer experience
One of the core benefits of APIs is that they enable businesses to free themselves from the time consuming and costly process of developing in-house software to power a single core application. Instead, developers can outsource certain tasks to remote “off-the-shelf” APIs, saving time, money, and allowing resources to be channeled elsewhere. These add-on services allow businesses to offer a more complete, one-stop solution to customers, whilst streamlining the process to optimise user experience.
Although we may not always realise it, APIs are playing a vital silent role in almost every purchase and interaction we have online. Take booking a holiday for example. As we browse comparison sights, APIs are working furiously behind the scene to aggregate information from airline databases, hotel websites, and excursion providers. The API performs the back and forth needed to retrieve the information, whilst we are able to sit back and view all of the results on the same page. Simplifying this process enables travel comparison websites to make the search for holidays quick and easy, and encourages customers to stay on the site by offering all that they need in one easy to consume package.
APIs also allow smaller businesses to utilise tools provided by some of the world’s largest and most successful companies. Google’s Calendar API for example could be used within a beauty salon website to enable customers to book and schedule treatment reminders, whilst Apple’s weather tool could be plugged-in to an events company website to give customers real-time weather updates. While the API’s developer does retain ultimate control over how the API is used, there are still countless ways to integrate these tools to benefit your business and improve the functionality of your website.
The recent Covid-19 pandemic in particular has highlighted the value of an API class that normally receives little attention; communication APIs.
Today, companies are boosting spending on unified communications-as-a-service (UCaaS), along with video conferencing, collaboration, and voice technology solutions given the exponential growth in home and remote working as a result. Where face-to-face contact is limited by necessity, businesses need to be able to communicate with employees and customers in ways which are secure, simple, and cost-effective.
Given how rapidly the technology landscape changes, APIs are the clear solution to avoiding the expense of developing tools from scratch, in addition to harnessing the power of the advanced features offered by established API providers.
Using them, businesses are able to adapt to suit changing customer preferences; for example offering an online chatbot to handle customer queries, or by using multi-channel messaging to connect with customers via WhatsApp or Messenger. These tools are not only useful, but can also allow you to gain intelligence into a customer’s preferences and habits – both useful marketing gauges.
On the other hand, comms APIs can also help to address problems that may crop up internally within organisations and workforces. There are APIs which allow callers to automatically sync calendars, meaning that meetings will only be scheduled when all parties can attend. There are also APIs for timezone conversion, permissions requests, and for video link calls and messaging. With the work from home trend continuing for the foreseeable future, investing in these areas is critical if businesses want to keep delivering at the highest levels.
Considering all of the above, it’s clear that we can expect to see the adoption of APIs continue. Developers are constantly working to create increasingly sophisticated products, and many have moved towards exclusively building and hosting APIs, rather than building the apps themselves – creating a so called “API Economy” of sorts.
This focus on creating the best possible APIs has allowed smaller businesses to harness the collective expertise of the world’s largest and most successful companies, and the chance to use these tools represents a fantastic opportunity for growth. The reach of APIs extends far beyond the IT department, and with a basic understanding, they can be used by senior management and leadership teams to optimise all areas of the business – not bad for three small letters.
Unexplained Wealth Orders: Rightly Celebrated or Over-Rated?
By Nicola Sharp of financial crime specialists Rahman Ravelli considers the attention given to unexplained wealth orders – and emphasises that they can be challenged.
There is little doubt that many sectors of the media – and their readers – enjoy a story that involves an unexplained wealth order (UWO). They do, after all, have many of the ingredients that many look for in a good tale: allegations of wrongdoing on a large scale, someone being made to hand over assets worth more than most people will earn in a lifetime and the sense that justice has been seen to be done.
In the latest UWO, which was widely covered in the media last week, Leeds businessman Mansoor Mahmood Hussain was compelled to hand over property worth just short of £10M, after being accused of acting as a money launderer. He has been ordered to surrender the assets because the National Crime Agency (NCA) believed his wealth was the proceeds of crime, and so considered him a suitable target for a UWO.
Introduced by the Criminal Finances Act 2017, UWOs give law enforcement agencies powers to require persons to explain how they came to possess their assets, and to show that their wealth has come from legitimate sources. A UWO can be sought without any civil or criminal proceedings having begun. There is no need for the subject of a UWO to have been convicted of an offence or to have had a civil law judgement against them. Agencies can apply to the High Court for a UWO against any property valued at over £50,000, if the person owning it is reasonably suspected of being involved in serious crime (or connected to a person who is) and there are reasonable grounds to suspect that a person’s lawfully-obtained income would be insufficient to allow that person to obtain that property.
Like Zamira Hajiyeva before him, Mansoor Hussain’s inability to provide a credible, innocent explanation for his wealth has cost him – and generated headlines. Hajiyeva may be best known for somehow racking up £16M of expenditure at Harrods. But this only became known when she was the first person to be the subject of UWOs. The NCA expected her to explain how she had bought a £11.5M Knightsbridge house and a £10.5M golf course in Ascot, bearing in mind her husband is the former head of the state-owned International Bank of Azerbaijan, had a salary of no more than $70,000 and was convicted of fraud and embezzlement. Earlier this year, she lost her appeal against the UWOs, thus enabling the media to re-run her story and giving the NCA the chance to make approving noises about UWOs being a valuable tool in tackling illicit finance.
But before there is a rush to applaud UWOs, it should be said that the NCA’s relationship with them has been a chequered one, to say the least. Since becoming available to the NCA, the agency’s success rate with UWOs has been patchy. This is despite the standard of proof for UWOs being significantly lower than that required in criminal cases. Last year saw the NCA granted three UWOs for London property valued at £80M. Yet less than a year later, these UWOs were discharged, with a judge criticising the NCA’s “unreliable’’ assumptions and “artificial and flawed’’ reasoning. The Court of Appeal then refused the agency permission to appeal this decision.
While a UWO is a tool that enables law enforcement agencies to seize assets they believe are the proceeds of crime without anyone ever being convicted, it does not yet appear to have become the great weapon against illicit wealth that many would have hoped. Of the four cases begun since UWOs were introduced, two are still being contested. Mansoor Hussain’s case is the first time a UWO has successfully led to the recovery of assets from an individual.
Although, a UWO can be seen as effective in certain situations, it will often be considered the most (and perhaps only) viable option when a prosecution has failed or when the authorities do not believe there is enough evidence for a realistic chance of a conviction.
When being faced with an UWO it should be remembered that whilst agreeing to settle and hand over property is not an admission of guilt, anyone facing a UWO must consider carefully how they respond to the authorities. It is vitally important to take the right advice. Deciding how to proceed when assets worth millions are at stake can be the biggest decision a person ever has to make.
In such circumstances it will often be the case that an intelligent, robustly-argued challenge to a UWO – and, in particular, to the allegations being made by the law enforcement agency seeking the UWO – will bring success. But that success will depend on knowing precisely how to respond – and who to turn to – if and when you become the intended target of a UWO.
How Siloed Data Leaves Financial Institutions Open to Fraud
By Stephany Lapierre, CEO Tealbook
Reducing the risk of fraud is a top priority for all financial institutions since fraud is responsible for massive profit loss, as well as the degradation of an institution’s integrity and brand.
In trying to prevent fraud, most executives look to protect themselves from the outside in, implementing layers of security and launching reactive measures. However, in order to truly protect your organization from fraud, it’s imperative to begin by looking at your existing internal structures. The most critical and often overlooked area to assess is how your organization obtains, enriches, and distributes data.
Streamlining and scrubbing your data can increase profitability without adding to resource spend. Having good data allows you to complete your due diligence on vendors and external entities your organization regularly deals with. It favorably adjusts your efficiency ratio and reduces risk by eliminating redundancies, conflicting information, and information gaps. In addition, it allows smaller teams to operate with increased scale and effectiveness. In turn, this leads to a more effective vendor vetting process and less room for error in payment information verification.
Conversely, poorly managed data is confusing and deceiving and can play an unfortunate role in giving fraudulent access to outside parties through internal miscommunications. For example, updates could be made in one system and not another, and suddenly different departments are working with different data sets like payment information or legal formation documents that regulators look for in audits, and no one knows what is true or accurate. This effect snowballs over time, creating massive holes in the integrity of the data, creating unnecessary risk exposure and audit failures.
All of these vulnerabilities can serve as the foundation for developing a risk management protocol that may be rendered useless if it is based on poor data. It is impossible to properly vet vendors and suppliers or verify payment information if the data is unreliable.
By investing in a solid Data Foundation, you’ll see an increase in the success of your risk management and fraud prevention measures. In many instances, you won’t need to add more steps or resources, just power your existing systems with clean, agile, and accurate data to see improved efficiency.
Here’s a closer look at the most common vulnerabilities within a typical financial institution’s data ecosystem:
Fragmented Organization Structure
As organizations grow and scale, it’s inevitable that different subsections will become isolated from one another and begin different processes for data management. Poorly managed systems can exacerbate this lack of communication and threaten data integrity.
It may not seem like cause for concern if a few different arms of an organization aren’t completely in sync. However, in the financial space, this issue rarely applies to just one or two organizational divides. For example, a prominent US-based financial institution boasts over 90 business units, all of which need to be synergized in order to prevent inaccurate data, redundancies, and problems with regulatory information gathering. This siloed information is, unfortunately, a common practice that needs to be addressed.
Unmanaged Proprietary Systems
In an attempt to serve data in a highly specialized way, many institutions have explored developing proprietary data systems for internal use. However, because of factors like employee turnover or an inability to keep up with data integrity best practices, these legacy systems quickly become obsolete and unmanaged. Their custom nature also renders them inflexible and unable to integrate with other solutions.
When trying to work around an unmanaged system, different branches of an institution may turn to different solutions. When work is being done across different platforms, this reduces visibility and increases risk for inaccuracies, which leads to poor decisions, costly rework, and potentially fraud.
If your organization is reliant on a proprietary system, consider if that system is functional and scalable. If it’s not, you may want to look into a flexible data management system that can work with other technologies.
Disparate Information Across Systems
Mergers, acquisitions, and growth also lead to using and implementing many different ERP solutions and antiquated legacy software that are forced to communicate with each other using painful manual efforts. A major problem arises from the fact that these systems operate across numerous lines of businesses, all with different siloed data. By having so many siloed systems that could be compromised with harmful data, these disparate data sources leave banks and other financial institutions exposed to unnecessary risk.
Different departments have different needs, so it makes sense that they would use different solutions, but it’s important that those solutions pull from a single source of truth in order to prevent the types of data inaccuracies that lead to vulnerabilities.
Closing the holes in your data integrity is the most proactive way a financial institution can defend against fraud. As hackers get increasingly creative and aggressive, it becomes even more critical that organizations have a trusted Data Foundation to base their decisions on. This can be achieved by ensuring that siloed systems are powered by consistent and accurate data from a single reliable source.
Adoption of tech in private markets lags behind industry trends
Nine out of ten financial institutions have accelerated their digitisation strategy as a result of Covid-19. Yet just 26% of...
Covid-19 disruption drives five new retail supply chain trends
The business disruption caused by COVID-19 has resulted in four out of five (82%) retailers changing their approach to stock...
Remote leadership anxieties
It’s a difficult time to be navigating the complex world of business. Whilst adapting to new ways of working remotely,...
Online jobs soar by 14% in third quarter 2020, Freelancer.com’s Fast 50 reports
Freelancer.com (ASX: FLN), the world’s largest freelancing and crowdsourcing marketplace by number of users and jobs posted, today released the...
One third of money management tools face closure by the end of the year if they do not embrace open banking
New research from Yolt Technology Services shows 35% of Personal Finance Managers aren’t using any open banking technology Imminent screen...
Pivoting growth strategy to rebuild consumer trust and confidence
By Richard Steggall, the CEO of Urban FT Trust is essential to all relationships, whether personal or professional. And in...
Everything you need to know about APIs for business
By Omar Javaid, president, Vonage API Platform, Vonage If your work brings you into close proximity with technology, chances are...
Accountants have become critical to the survival of businesses and their reputations during Covid-19
The opportunity for fraudulent activity to flourish as finance departments operate remotely with less oversight in these extraordinary Covid-19 times...
Unexplained Wealth Orders: Rightly Celebrated or Over-Rated?
By Nicola Sharp of financial crime specialists Rahman Ravelli considers the attention given to unexplained wealth orders – and emphasises...
Taking advantage of the UK’s renovation revolution
By Paresh Raja, CEO, Market Financial Solutions UK property is a popular asset class because of its historical resilience to...