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By Evan Davies, CTO, Solera


Generation Z is reaching its mid-20s in 2021. This generation has grown up in an age when companies like Amazon, Uber, and Airbnb use AI to simplify purchasing, communicate directly with customers, and apply analytics to their business. Gen Z probably doesn’t remember services or products without voice activations, personalized buying suggestions, and chat bots offering instant interaction. As they become more influential as consumers, their expectations for services and interactions will include many AI-based offerings. This, combined with the purchasing power of Millennials will require all businesses to evolve if they want to meet these collective expectations for technology and rise to the new standard of on-demand interaction.

To come to terms with this, the insurance industry is quickly adapting and integrating AI into business operations and claims infrastructure. In a recent report, McKinsey projected that there is a potential annual value of up to $1.1 trillion in additional business revenue if AI is fully embraced in the insurance industry alone. The sector itself has been slow to mobilize digital transformation in the past, but the disruption seen in the Covid-19 era has pushed the adoption of AI and other next generation technologies up the priority list for many insurers. That said, it’s not as simple as adding something like visual intelligence to the photo estimation process.

Computer vision and the technology that enables it must be underpinned by two fundamental elements: quality data and knowledge of vehicle repair processes. AI-based solutions must be trained and supported by the combination of these two components to ensure consistency of results at every stage. Only then can insurers advance their algorithms to maximize accuracy, ensure faster claims resolution and enhance the customer experience.


Evan Davies

Image Recognition and Processing Using AI

For those who have recently filed an insurance claim of any kind, you are probably familiar with the image capture and upload process. While image capture is not necessarily new, the technology at work evaluating and producing the outcomes is quite new and can be complex.

Automation tools improve the first notice of loss (FNOL) and triage processes by speeding up review of damaged photos, identification of total loss vehicles and supports identification of the next best action for repairable vehicles. This means that decisions that affect claims outcomes for the insurer, repairer and insured are made at the beginning of the claims process to streamline the workflow and speed up the claims and repair process for all involved.

When it comes to AI photo based estimating, there is no ‘one-size-fits-all’ solution with more than one way to approach the process. One approach is to train AI models by learning from historical claims data and related damage photos. Another approach is to use a training data set containing annotated or “labeled” photos.


Get Quicker and More Reliable Repairs

In addition to customer experience, AI is enabling insurers to maximize the skills and outputs across their own workforce. In the absence of an AI-based estimating system, trained appraisers are required to produce repair estimates. However, with the use of an AI-system, those skilled workers can be reassigned to more complex cases.

Providing an accurate estimate of the size, position, and severity of the damage plays a crucial role in getting the repair cost correct. There are many variables that influence the cost – from material and geometry to accessibility and thickness. Modern vehicles are made of alternative materials, such as different types of steel, aluminum, and carbon fiber, which inevitably impacts cost. Many new vehicles are also fitted with sophisticated advanced driver-assistance systems (ADAS) which need to be considered when assessing damage. This is a lot to consider and, hence, why the repair science element of any AI-based estimating is so important for safe repairs and correct claims valuations.


Blending Data and Repair Science

As with any automated technology, the algorithms that power insurance solutions must be trained and supported by the right set of data to ensure consistency of results at every stage and for every touch point served by AI. It’s not just about bringing AI to an existing workflow. Estimating using AI is about blending repair science technology with a database of historical claims and images gathered over years of servicing and supporting the industry that makes this evolution so powerful.

The combined approach of these two types of data enhances machine learning algorithms to drive efficiency and accuracy. It provides an holistics and integrated approach that can effectively increase accuracy and performance across an entire claims workflow, compared to simple AI image recognition point solutions.


Future Proofing for Digital Native Generations

Gen Z are pushing the boundaries of digital communication once again – but we know it won’t stop there. Now, more than ever, with the increasing use of digital channels by all generations and younger generations’ expectation for it, AI is becoming trusted by service providers around the world, including those in the insurance industry. Implementation of next generation solutions like the AI now found in many repair estimating solutions is accelerating and must continue doing so if insurers are to navigate ever-changing consumer needs.

It’s widely acknowledged that this is the future of insurance and beyond. Yet, with any new technology adoption, it doesn’t come easy. It takes partners with the right background and expertise to help chart the path, expand adoption and rewrite the standard of service for years to come.


Top 10

An Entrepreneur’s Guide to Investing in Bitcoin




Marcus de Maria, Founder and Chairman of Investment Mastery.


Over recent years, Bitcoin has been steadily growing in popularity among today’s investors. At the same time, there has been a lot of debate about Bitcoin, and other cryptocurrencies, and their value.

Its supporters argue that it is the future of currencies and investment; its critics are adamant it’s not all it’s cracked up to be and might not make the big profits people are expecting.

To better understand its true stature in the market, we need to look at recent developments. For instance, Bitcoin’s valuation has risen by more than 763% in just one year, easily surpassing the rise in the traditional stock market.

With more and more people buying Bitcoin, it is now gaining the attention of the mainstream financial institutions and platforms, when once Bitcoin was derided, joked about and said would never last.

Marcus de Maria

Fast forward twelve years since its’ launch, and we have Tesla and SpaceX mastermind Elon Musk recently announcing that his car empire will not only buy $1.5 billion-worth of Bitcoin, but will accept cryptocurrencies as payments in the future.

And well-known FinTech companies such as Square and PayPal have also announced their intention to support Bitcoin in the future.

Despite this, the most important Bitcoin development is, perhaps, the recent initial public offering (IPO) of Coinbase Global, Inc. (NASDAQ: COIN), today’s leading cryptocurrency exchange platform.

There is no doubt: Bitcoin is gaining momentum. Recent developments have contributed to the sharp rise in the value of Bitcoin, and asset proponents believe this is just the beginning.


Bitcoin background

Bitcoin was created in 2008 by a programmer, or group of programmers, under the pseudonym “Satoshi Nakamoto”. Twelve years on, and the true identity of Bitcoin’s inventor is still unknown, adding a little mystique to this already enigmatic entity!

Essentially, Bitcoin is a cryptocurrency. A cryptocurrency is a virtual “coins” or “tokens” and used in digital cryptocurrency systems instead of physical cash.

Similar to physical fiat currencies, digital coins have no intrinsic value, and are not backed by gold or silver.

Bitcoin is one of the most widely used of the thousands of cryptos now available to the investor.

Considering that the great attraction to crypto is that it’s a decentralized currency, thousands of different types of coin in “circulation” is a big giveaway to how popular it is among users and investors.

What gives Bitcoin its value is the fact that there will only ever be 21 million bitcoins “minted” or “mined” to give its proper definition (more on this in the future).

It’s this scarcity that provides the value, although one Bitcoin can consist of multiple denominations, the smallest being a “satoshi” which is 0.00000001 of one Bitcoin (or BTC as it is also known).


Bitcoin & The Blockchain: How does it work?

Bitcoin exists solely on the “blockchain” in “wallets.”

A wallet is the digital equivalent of a traditional bank account for fiat currencies such as dollars, sterling, yen, etc.

The blockchain is a public ledger that is totally transparent and accessible to everyone who uses the blockchain and bitcoin, and now any crypto that is in existence.

Transactions on the blockchain are “peer-to-peer”, meaning the transaction doesn’t go through a “middleman” (i.e. third party that would normally charge a fee for making the transaction).

Crypto transactions also undergo thorough verification and confirmation.

Crucially, every transaction and record of bitcoin activity is encrypted which means no one knows who owns any one bitcoin or where it goes to and from, unless they publically declare it (although the identities can eventually be detected under special police powers in cases of suspected fraud).

Only the transaction itself is recorded and is made visible to anyone.

That is why Bitcoin is a cryptocurrency (or crypto), because it has an extremely high level of privacy to it via cryptography.

“Crypto” comes from the Greek word “kryptos,” meaning hidden.

Bitcoin wallets operate via secret key.

This key is used to “sign” transactions. It provides mathematical proof that the transaction has come from your wallet (or owner of the transacting wallet).

This secret verification stops the transaction from being tampered with once it has been issued.

All transactions are confirmed and appear on the block chain network within 10-20 minutes.

It is this security and the fact YOU – and not the banks – are truly in control of your digital money that is so appealing to users and investors alike.


What to consider when investing

Firstly, and arguably most importantly, is risk-factor. Investing in Bitcoin as an individual is a lot less risky than investing as a business.

The mentality must be, ‘this is my business’s money. I won’t speculate with my business’s money, and I am not going to risk my employee’s livelihoods. Yes, I would be crazy not to invest but it would be crazier to risk it all.’

It’s very easy to go all-in and invest a large sum of money when you have it, but that is not really a sensible strategy.

So, to start with, entrepreneurs and business leaders should consider the risks, diversifying their portfolio and starting small.


Other Bitcoin Investment Options

There are different options when it comes to investing in Bitcoin.

First, you can invest in a company that uses Bitcoin technology so you will be exposed to it without purchasing it directly. When the value of Bitcoin goes up, the company shares go up too, providing a return on your investment.

I can’t invest in Bitcoin through my ISA, but investing in a company such as Block (previously known as Square) means I have an indirect tax-free investment opportunity in Bitcoin. Investing in a company that utilizes Bitcoin can be more volatile than Bitcoin itself, so more money can certainly be made.

Investing solely in Bitcoin is different, as it doesn’t move so much in value, but the individual company using Bitcoin can go up and down sometimes by 80%.

Buying Bitcoins directly from an app like Coinbase allows investors to “physically” own the asset.

This is an important distinction to make, as Coinbase allows investors to actually buy Bitcoin and store it in their own crypto wallet. That way, investors will be able to gain access to the coin’s price performance and use it as the currency to make other trades.

Owning a standalone Bitcoin is no different from owning any other currency, except for the incredible fluctuations in value.


To invest directly into Bitcoin here’s how to get started:

  1. Sign up to an Exchange
  2. Enable two-factor-authentication for security
  3. Get a Bitcoin wallet
  4. Connect the wallet to a standard fiat bank account
  5. Place your Bitcoin order
  6. Manage your Bitcoin investment

When the set-up is complete, what you really need to consider is, how much do you know? I am a firm believer in spending at least 20 minutes a day educating myself on investing. I’ve seen too many beginner investors ignoring that advice and rushing in without understanding how it all works.

Surround yourself with people that understand crypto investment and dedicate time to reading up on strategies and tips that will benefit all investments you make.

Bitcoin is certainly a crypto asset you should be investing in alongside a diversified portfolio. It is certainly a highly volatile asset with large and rapid price swings, which in turn can offer the potential for large returns but also carries a high level of risk.

Before making any decisions, it is critical that you learn how to invest in Bitcoin responsibly and utilise proven, reliable strategies. Once you feel confident with your approach, take that first brave step.

As Warren Buffet once famously said, “Be fearful when others are greedy, and greedy when others are fearful.”

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The role of Artificial intelligence in compliance at banks




Sujata Dasgupta, Global Head – Financial Crime Compliance Advisory, Tata Consultancy Services


There’s not a financial institution across the globe that will be a stranger to money laundering, fraud, and other financial crimes. In the UK, institutions have witnessed a significant rise in financial crime since the pandemic, with analysis by of the fraud and cyber-crimes reported to Action Fraud finding that £2.4 billion was stolen in 2021, 174% more than the previous year. And these are just those crimes that are reported– in reality, the amount lost to criminals could run to tens or hundreds of billions of pounds per year.

With the frequency and severity of such crimes on the rise, they increasingly pose a significant challenge to banks and financial services firms. Many banks have been struggling to tackle longstanding financial crime compliance issues due to poor data quality, fragmented compliance platforms, high false alert volumes, and high operational costs.

But the biggest problem is that the financial crime compliance units of these financial institutions still rely mainly on heavy manual processes. Their key reason for their cautious approach in the utilisation of AI and automation has been uncertainty about technology. Do regulators approve machine-based decision-making, and is machine learning logic fair in identifying suspicious activities?

This uncertainty highlights a clear need for the proper utilisation of technology in financial crime compliance. Financial crime compliance functions are responsible for monitoring account transactions and customer behaviour for money laundering, terrorist financing, bribery, corruption, and fraud. The current systems and processes of the financial institutions mean that these tasks involve heavy manual routine measures that take up a significant portion of staff’s working time.

Regulatory technology, or RegTech, could make handling these routine tasks more efficient. This tech can utilise machine learning, advanced analytics, NLP, dynamic biometrics, network graphs and other forms of AI, which can take responsibility for routine tasks such as data collation and processing, as well as some other everyday responsibilities. This can free up significant portions of analysts’ time for more complex, cognitive work such as in-depth investigations into potential crimes, as well as other tasks that require data-driven judgement and decision-making.

Lack of access to information on criminal activities outside national boundaries has hindered efforts to combat banking fraud and compromised anti-money laundering and financial crime compliance actions taken by banks. While the need for collaborative action in fighting financial crime is evident, financial crime intelligence sharing has hit a roadblock given restrictions imposed by data privacy regulations. As usual, technology has come to the rescue – privacy-enhancing technologies (PET) offer a way to share data while protecting privacy. Using PETs can help financial institutions to understand suspicious patterns of behaviour through financial information sharing and analytics whilst preserving the privacy of individuals.

Internationally, governments are just now starting to utilise PETs. For example, in June 2022, the UK and US governments collaborated to develop PETs to tackle digital financial crime and enable data sharing better across borders to prevent money laundering attempts.

There has been some progress in using technology to fight financial crime on a national level also. In 2019 the Bank of England announced that it would be working to encourage the introduction of artificial intelligence-based RegTech among UK banks. It pledged to launch a review in consultation with financial institutions to increase the use of RegTech over the next decade, to reduce the burden on them and improve the quality and effectiveness of their data. UK’s Regulator, Financial Conduct Authority (FCA), has encouraged FIs to use advanced technology to prevent and detect fincrimes, providing sandboxes for RegTech solutions development and validation. FCA has been organising Tech Sprints every year to foster innovation in this space.

Some banks in the UK have already started adopting AI-powered solutions in fighting financial crime, while others are yet to explore advanced technology in this space. As financial crimes quickly grow more complex, adding more people to compliance functions alone may not help in disrupting them. A strong defence requires a combination of people, processes, and platforms. Processing huge volumes of data to uncover sophisticated criminal networks and illicit money trails in financial institutions must necessarily utilise AI.

In 2021, the European Commission announced guidelines on ethical AI, pursuant to which artificial intelligence solutions will be classified as a minimal, limited, high or unacceptable risk. Recently the UK Government also announced plans to publish a national strategy on responsible AI adoption. These types of guidelines based on regulation will support increased AI usage even in compliance functions.

In the UK, people have trust in banks and financial institutions. The only way for the financial sector to maintain and grow this trust is to keep up with the development of technology which can help fight financial crime. Financial institutions must step up and proactively seek technological and AI solutions to deal with increasingly sophisticated criminals and hackers. The time for action is now!

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