Companies House 2025 IDV Changes: Everything you Need to Know

Money laundering is a significant global challenge, enabling criminals to legitimise dirty money derived from illegal activities. This process undermines economies, supports criminal activity, and weakens the legitimate financial system. Financial institutions and organisations operating in high-risk industries must adopt Anti-Money Laundering (AML) measures to combat this pervasive issue.

One of the most effective ways to address this challenge is through AML integration, a proactive approach that embeds AML compliance tools and processes directly into organisational workflows. By understanding the three stages of money laundering and how AML integration works to detect and prevent suspicious activities, organisations can build a robust defence against financial crime.

What Are the 3 Stages of Money Laundering?

The money laundering process unfolds in three distinct phases: placement, layering, and integration. Each stage plays a critical role in disguising the illegal origins of funds and reintroducing them into the legitimate economy.‍

Placement

Placement is the first stage of money laundering, where illicit money enters the financial system. This stage involves depositing cash into banks, converting it into other assets, or using cash-intensive businesses to blend it with legitimate earnings. Examples include depositing small amounts of money across multiple accounts (a process called "structuring") or purchasing goods that can be resold to generate clean income.

This stage is particularly risky for criminals, as it involves the direct handling of dirty money. Financial institutions can detect placement activities by implementing strict reporting thresholds and monitoring large cash deposits. Having a secure AML integration ensures such systems are in place to identify unusual patterns early.

Layering

Layering seeks to obscure the origins of funds by conducting multiple, complex transactions. This stage may involve transferring money through multiple accounts, buying and selling high-value assets, or moving funds to offshore accounts. Criminals often use shell companies and fake invoices to legitimise these activities.

The goal here is to create a tangled financial trail that is difficult for regulators and law enforcement to follow. Advanced AML systems equipped with artificial intelligence can analyse transaction patterns to flag potentially suspicious behaviour, making it harder for criminals to successfully layer funds.

Integration

The 3rd stage of money laundering, integration, is where laundered money re-enters the legitimate economy. At this point, the funds appear clean and are often used to acquire assets like real estate, luxury goods, or investments in legitimate businesses. This stage is particularly challenging to detect, as the money appears to be from a legitimate source.

An AML integration plays a vital role in monitoring these high-value purchases and investments, ensuring that organisations comply with money laundering AML regulations and prevent the completion of the laundering process.

The 3 Stages of Money Laundering Explained

To combat money laundering effectively, organisations must address each stage of the stages of the money laundering cycle. This requires a comprehensive AML strategy that combines technology, processes, and regulatory expertise.

At the placement stage, financial institutions must focus on monitoring large or unusual cash deposits. Tools like transaction monitoring systems and Know Your Customer (KYC) procedures can help detect early signs of criminal activity. During layering, the emphasis shifts to tracking complex transactions and uncovering attempts to obscure the origin of funds. Finally, at the integration stage, due diligence and ongoing monitoring are crucial to identifying suspicious investments or acquisitions.

By addressing all three stages through a well-integrated AML framework, organisations can significantly reduce the risk of financial crime.

Common Methods of AML Integration

AML integration involves embedding anti-money laundering measures into every aspect of an organisation’s operations. This approach ensures a seamless and proactive strategy for identifying and addressing financial crime.

One critical component of AML integration is KYC processes, which verify customer identities and assess their risk profiles. By ensuring that businesses have a thorough understanding of their clients, KYC procedures make it harder for criminals to conceal their identities.

Another key element is transaction monitoring, which involves real-time analysis of financial activities. Modern AML solutions use artificial intelligence (AI) to detect anomalies and patterns that may indicate suspicious activity. These systems can flag unusual transactions, such as large sums of money being transferred to high-risk jurisdictions, allowing organisations to take immediate action.

Collaboration is also a vital aspect of AML integration. Data-sharing platforms enable financial institutions to exchange information securely, fostering greater cooperation with regulators and law enforcement agencies. This collaborative approach enhances the overall effectiveness of AML efforts, particularly in detecting cross-border laundering schemes.

Common Challenges in AML Integration

Implementing AML measures is not without its challenges. One major hurdle is navigating the complex and ever-changing regulatory landscape. Key frameworks include:

• 6AMLD (Sixth Anti-Money Laundering Directive): Introduced stricter penalties for non-compliance and expanded the list of predicate offences.
• FATF Recommendations: A set of international guidelines for combating money laundering and terrorist financing.
• BSA (Bank Secrecy Act): US legislation requiring financial institutions to report suspicious transactions to the government.

Failure to comply with these laws can result in severe penalties, reputational damage, and even business closure.

Staying compliant with these regulations requires significant resources and expertise. Failure to do so can result in heavy fines, reputational damage, and even legal action. To address this, organisations must adopt flexible AML systems that can adapt to new regulations as they emerge.

Another challenge is the rapidly evolving nature of financial crime. Criminals are increasingly leveraging technology to create sophisticated money laundering schemes, from using cryptocurrencies to conducting large-scale fraud. To stay ahead, organisations must invest in cutting-edge tools, staff training, and partnerships with other entities in the fight against financial crime.

Emerging AML Trends

The fight against money laundering is being reshaped by technological advancements and new regulatory approaches. Some emerging trends include:

• Blockchain Technology: Offering transparency by creating immutable records of financial transactions, blockchain can help identify and prevent laundering activities.
• Real-Time Analytics: Cloud-based systems that monitor transactions instantly, allowing for proactive intervention.
• Biometric Verification: Enhanced customer identification through biometrics like fingerprint or facial recognition.

Why AML Integration Is Essential

The integration of AML measures into an organisation’s workflows is no longer optional, it's a necessity. AML integration enables businesses to detect and prevent money laundering at every stage of the cycle, from placement to integration.

One key advantage of AML integration is its ability to provide a comprehensive view of financial activities. By consolidating data from various sources and applying advanced analytics, AML systems can identify patterns and trends that may indicate financial crime. This not only helps businesses comply with regulations but also protects their reputation by demonstrating a strong commitment to preventing illicit money from entering the economy.

However, while AML integration is highly effective, it is not a silver bullet. Organisations must complement their AML efforts with ongoing staff training, robust internal policies, and collaboration with regulators. A holistic approach ensures that businesses are well-equipped to tackle the complex and evolving challenges of money laundering.

How FullCircl can help

FullCircl offers advanced AML solutions that enable organisations to meet their compliance obligations while staying ahead of evolving threats. Our comprehensive approach integrates state-of-the-art technology with expert insights, helping businesses detect and prevent money laundering across all three stages of the process.

FullCircl empowers organisations to safeguard their operations against financial crime. By choosing FullCircl, businesses can ensure compliance with AML regulations, protect their reputation, and contribute to the global fight against money laundering.

To learn more about how FullCircl can transform your AML processes, contact us today.

In recent years technological advancements, changing client expectations and global economic dynamics have continually shifted the financial landscape. As a result, data and insight has emerged as a vital tool for empowering wealth managers to make informed decisions and deliver personalised financial services.

This demand has driven the global WealthTech solutions market, which has grown at a compound annual growth rate (CAGR) of 14.7% to reach $5.42 billion in 2024, and is forecast to reach $9.43 billion by 2028. Key trends highlighted by an annual WealthTech solutions market report include the adoption of AI and machine learning, and importantly the adoption of big data analytics.

Why such demand for data?

By surfacing and analysing large volumes of structured and unstructured data, wealth managers can build a comprehensive view of market trends, customer behaviours, potential risks and valuable investment opportunities to build tailored financial solutions that meet individual client needs, appetites, and goals.

Personalisation has always been a cornerstone of wealth management, but as we move into 2025 the demand for customised financial strategies is set to increase exponentially. A recent survey by Deloitte found that 76% of asset managers expect to increase their offerings of customised products over the next three years.

But there’s a caveat…

At a time when access to real-time data and insight is vital to wealth manager success, too often market, client, and portfolio data is scattered across different systems, leaving them struggling to gain a single source of the truth. This impacts agile decision making by making it difficult to identify opportunities, spot risks, and design tailored investment strategies.

What does an advanced data-led approach to personalised wealth management look like?

From prospecting to onboarding and ongoing management, leading wealth management firms are turning to a single data orchestration platform to not only improve service quality, but also drive competitive edge by adopting a smarter, data-first approach to managing and growing client relationships.

Let’s look at the benefits…

1. Find the right customers – segment clients according to ideal profiles, financial objectives, and risk profiles
2. Single customer view – combine personal data, financial behaviour, market and asset performance data, with a deep understanding of customer preferences, risk tolerance, investment goals and even sustainability concerns
3. Personalised advice – harness holistic insights to design portfolios and tailor specific fund recommendations aligned perfectly to individual goals and risk appetites, and continuously finesse based on customer profile changes, economic shifts and market conditions
4. Personalised engagement – real-time insights combined with a deep 360-degree understanding of customer needs allow for timely outreach and opportunities that are always laser-focussed on individual client needs
5. Risk management – data technologies are playing a key role in helping wealth managers identify and mitigate risks – PEPs and sanctions, UBO data, adverse media, identity verification and so on, which can assist in assessing risk in real-time, adjusting portfolios appropriately.
6. Efficiency and productivity - significantly reduce the time required to research and analyse a company, and instead focus efforts on building comprehensive advice based on advanced data-driven decisioning. Harnessing data and insights also empowers wealth managers to scale-up, and actively target and advise a broader client base at a lower cost
7. Grasp emerging opportunities – ESG considerations continue to grow in prominence (a study by Preqin found that ESG will play a 77% more significant role in investment decisions by 2025). The ability to harness ESG scores, risk data, adverse media ESG performance insight etc. in investment decisions will deliver clear competitive advantage.

Wealth managers that can grasp these benefits and embrace emerging trends will be best positioned to cement their place as trusted advisors and thrive in the years ahead. In fact, Forbes recently described a future-ready wealth manager as one that can stay “agile and adapt to the new realities of the financial landscape by continually providing exceptional value to their clients and help them navigate an increasingly complex world of wealth management.”

Something also backed up by Deloitte who state that the successful wealth managers of the future will be those who can effectively blend human expertise with technological innovation, providing tailored solutions that meet the diverse needs of an increasingly sophisticated investor base.

How FullCircl can augment the wealth managers of the future

FullCircl’s solutions (in particular SmartBanker, SmartOnboard and W2 Global Data) can help wealth managers align data-driven insight’s with advanced analytics for a client-centric approach. Augmenting the expertise in delivering highly personalised advice and services to build trust, enhance customer experiences, improve outcomes, mitigate risks and foster long-term relationships.

• Targeted customer acquisition: identify untapped potential and establish meaningful relationships with new prospects. Harness actionable insights that enable personalised strategies that boost acquisition, customer loyalty and drive growth.
• Enhanced client onboarding: automate and enhance KYC/KYB and AML compliance activities and automate screening by integrating data from multiple sources – this identifies potential risks earlier and streamlines the onboarding of high value client whilst ensuring regulatory compliance.
• Proactive relationship management: surface real-time insights – matched, analysed and verified – to monitor portfolios, anticipate changes and opportunities, provide tailored advice and foster experience-based relationships.  
• Risk management: customisable rules and alerts, alongside PEPs, sanctions and adverse media screening and advanced identity verification help identify and mitigate risks that could impact regulatory non-compliance and reputational threats.
• Improved operational efficiency and lower cost to service: save time and resources by automating data and insight collection and analysis, giving wealth managers more time to focus on client interactions and strategic planning.

A best practice case study in data-driven personalisation

FullCircl has been working with one of the UK’s largest wealth management businesses, helping its team boost personalisation in the provision of investment solutions, portfolio management, wealth planning and advisory services.  

Their private and intermediary clients have unique requirements when it comes to structuring their wealth, estate planning, investment structuring, tax planning, future business exit and succession planning. As well as philanthropy, charity investments, and increasingly alignment with environmental, society and governance (ESG) considerations.  

FullCircl assisted the team in a number of methods:

• Finding new business owners to target: Access to data and insight on over 5 million companies and their ownership structures, using our graph technology and rules engine to match and filter companies and individuals based on specific financial parameters.
• Reasons to engage: Contextual and actionable news and social media insights, as well as business and market changes that will resonate with a business owner’s wealth objectives and preferences. FullCircl worked with the business to develop bespoke engagement signals unique to their needs.
• Access advanced company and individual data for prequalification and onboarding checks: Group structure, PEPs and sanctions, UBO data as well comprehensive and verified company and financial data, such as M&A activity and fund-raising activities.
• Identifying priorities to grow and protect wealth: Company and individual insights as well as market news provides new opportunities to cross-sell and upsell-services, as well as respond quickly to changing needs.
• Collaborate with the wider business: Helping the business as a whole respond to the needs of high net-worth business owners and the companies they operate.

And the results speak for themselves…

78% of the team report that FullCircl has helped them create new prospects, progress opportunities and close business. 75% of wealth managers believe we’ve also helped them save up to 2 hours per week, and as a result 88% of wealth managers believe the ROI of FullCircl meets or exceeds expectations.

Heard enough?

To learn more about how FullCircl can help your wealth managers boost performance and drive competitive edge, get in touch with a member of our banking success team for a demo today.

In a world increasingly driven by digital interactions, ID verification, otherwise referred to as ID&V has become a cornerstone of secure and trustworthy transactions. Whether onboarding a new customer, complying with AML regulations, or preventing fraud, businesses rely on effective ID verification processes to ensure seamless and compliant operations.

This ultimate guide will explore the fundamentals, best practices, and innovations in ID verification to empower businesses to make informed decisions.

What Is ID Verification?

ID verification, short for identity verification, is the process of confirming that an individual’s claimed identity matches their true identity. It often involves validating government-issued identity documents, such as passports, driving licences, or national identity cards, and sometimes cross-referencing these documents with other data points like proof of address.

By employing ID verification, organisations can:

• Prevent fraudulent activities such as identity theft and account takeovers.
• Comply with legal and regulatory standards, including anti-money laundering (AML) and customer due diligence (CDD) requirements.
• Streamline onboarding processes by automating document verification.

Why do businesses need ID verification?

There are several critical reasons why businesses require robust ID verification processes:

• Prevent Fraud: Identity theft is a growing concern, particularly for online businesses. Effective ID verification helps organisations detect and mitigate fraudulent attempts in real time.
• Compliance: Adherence to regulations such as AML, GDPR, and industry-specific standards demands precise and consistent ID verification.
• Onboarding Efficiency: Organisations can improve the onboarding process by ensuring quick and secure verification of new customers or employees.
• Customer Trust: Reliable ID verification services reassure customers that their personal data is handled securely.

Types of ID Verification

Manual ID Verification

Traditional manual ID verification involves physical checks of identity documents. This method remains relevant for face-to-face interactions but has significant drawbacks:

• Time-Consuming: Verifying documents manually can delay the onboarding process.
• Prone to Errors: Human error increases the risk of oversight or misjudgement.
• Scalability Issues: Handling large volumes of verifications is challenging.

Online ID Verification

Online ID verification leverages digital tools to authenticate individuals remotely. It is faster, more efficient, and widely used in industries like fintech and e-commerce. Advantages include:

• Real-Time Results Returned: The ability to verify IDs instantly.
• Global Reach: Capability to handle non-UK or non-EEA applicants seamlessly.
• Enhanced Accuracy: Advanced algorithms ensure precise validation.

Remote ID Verification

Remote ID verification allows businesses to authenticate users without physical interaction. This method is critical in a globalised and digital-first landscape, particularly for sectors such as banking and remote hiring.

What document are valid for ID verification?

The types of documents considered valid for ID verification vary by jurisdiction but commonly include:

• Government-Issued Documents: Passports, national ID cards, and driving licences.
• Proof of Address: Utility bills, bank statements, or official correspondence.
• Specialised Documents: Documents tailored for specific verifications, such as DBS ID verification requirements.

For non-UK or non-EEA applicants, acceptable documents may include:

• Residence permits.
• Work visas.
• International passports with supporting evidence.

How does the ID Verification process work?

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Step 1: Submission of Documents

Individuals provide scanned or photographed copies of their identity documents. For online ID verification, this is typically done through a secure portal or app.

Step 2: Document Verification

The submitted documents are analysed for authenticity. Businesses can use ID verification software checks for:

• Matching personal details.
• Validity of the document’s expiration date.
• Signs of tampering or forgery.

Step 3: Cross-Referencing

Details are cross-referenced with official databases to validate the user’s identity. Electronic identity verification tools enhance this step by integrating multiple data sources or by leveraging facial comparison to reduce fraud.

Step 4: Compliance Checks

The compliance team ensures that the verification adheres to regulatory requirements, such as GDPR and AML guidelines.

Step 5: Results Returned

The outcome is delivered to the requester, typically within minutes for digital systems.

For more information on how to effectively prove someone's identity, read the UK Government guide.

ID verification compliance & guidelines

Businesses using ID verification services must ensure they comply with key regulatory frameworks:

• DBS ID Verification: For roles requiring criminal background checks in the UK, ID verification must meet Disclosure and Barring Service (DBS) standards.
• GDPR Compliance: Online processes must prioritise data security and user consent.
• AML Regulations: Organisations in finance and real estate are mandated to perform stringent checks to prevent money laundering.

Ensuring compliance not only avoids penalties but also builds trust with customers and stakeholders.

ID verification best practices

To optimise ID verification processes, consider the following best practices:

• Utilise the Latest Technology: Leverage tools like AI-powered IDV software and digital identity platforms for precision and speed.
• Streamline the Onboarding Process: Minimise friction with user-friendly interfaces and intuitive workflows.
• Customise for High-Risk Cases: Deploy enhanced measures for high-risk individuals or transactions.
• Maintain Clear Documentation: Keep thorough records to demonstrate compliance during audits.
• Train Your Team: Equip the compliance team with the skills and knowledge to handle complex verifications.

The Future of ID Verification and Trends

Biometric Solutions

Biometrics, such as facial recognition and fingerprint scanning, are revolutionising the identity verification process. These solutions offer unparalleled accuracy and convenience.

Artificial Intelligence

AI-driven ID verification software enhances fraud detection by analysing patterns and anomalies that humans might miss. It ensures seamless operation even in high-volume scenarios.

Blockchain Technology

Blockchain’s decentralised nature provides a secure and immutable way to store digital identity data, enhancing privacy and reducing the risk of data breaches.

Real-Time Verification

With the demand for instantaneous processes, real-time solutions are becoming the norm, ensuring both security and efficiency.

In an era where trust and security are paramount, ID verification is no longer a luxury but a necessity. By adopting cutting-edge IDV tools, businesses can:

• Prevent Fraud: Safeguard against identity theft and other fraudulent activities.
• Ensure Compliance: Stay ahead of evolving regulatory demands.
• Enhance Customer Experience: Provide a seamless and secure onboarding process

The future of ID verification lies in leveraging advanced technologies to build secure, efficient, and customer-centric systems. Embracing these innovations will position businesses as leaders in their industries, equipped to handle the challenges of tomorrow.

Contact FullCircl today to learn how we can support your ID verification goals or download the 2025 State of IDV report to stay ahead of consumer trends.

In 2024 Britain’s banks – from big banks to mid-tier, challengers to fintech disruptors – have been upping their investment in Customer Relationship Management (CRM) Software.

The banking CRM software market has experienced rapid growth in the last twelve months. Expanding from $13.13 billion in 2023 to $15.40 billion in 2024. The current compound growth rate (CAGR) of 17.3% means banking CRM investment is expected to reach $29.39 billion withing the next four years.

Several factors are fuelling the current growth in banking CRM investment, but two key things stand out in particular – an increasing demand for automated customer insights, and a rising desire to improve customer service.

An increasing demand for automated customer insights

CRM solutions, such as Salesforce, Microsoft Dynamics, and HubSpot, have become an indispensable part of the banking technology stack. The ability to store and retrieve vast amounts of data, coupled with advanced reporting, campaign tools and countless other features has improved sales and marketing efficiency and transformed customer service.

But CRM solutions are static environments, and with customers’ worlds so highly dynamic, banks are increasingly turning to third-party data integrations to automatically  augment their CRM environments for more informed decision making.

A rising desire to improve customer service

Delivering exceptional customer service has become essential to banking profitability. The desire to improve customer service is being driven by a need to improve trust and reliability, enhance efficiency, reduce cost to acquire and serve, and deliver personalised interactions at scale.

CRM environments augmented with real-time data and insight from leading third-party data integrations, such as FullCircl, are fast becoming a bedrock of banking success – improving prospecting, enhancing sales and marketing interactions, personalising product and service delivery, maintaining satisfaction, delivering a cohesive customer lifecycle experience, and even predicting future needs.

This forward-thinking approach to CRM investment is allowing banks to stay ahead of customer expectations, be more agile in their decision-making, and continuously innovate and differentiate their offerings.

In what ways can third-party data and insight drive more value from CRM investments?

1. A single version of the truth

Consolidating data about prospects and customers from an incredibly wide or sources into a unified record is vital. Banks no longer need to struggle with poor quality data and unstandardised data. With the right strategy and CRM data integrations banks can achieve a unified customer view -including ownership data, company structure etc - and therefore deliver a unified customer experience with consistent interactions across every touchpoint throughout the relationship lifecycle.

2. Advanced prospecting and segmentation

Identifying and securing new opportunities for growth remains a challenge. By accessing advanced customer data banks can better analyse their total addressable market, identify untapped opportunities, and segment prospects more efficiently. They can therefore devise more tailored sales and marketing outreach.

3. Data-Driven decision making

Utilising data integrations and advanced analytics to provide deeper insights into customers helps banks make more informed and agile decisions. Further harnessing AI analyse this data ensures banks proactively address customer needs and expectations, proactively strategising next best actions for maximum impact and value.

4. Real time interactions

The ability to have customer profiles continually refreshed with the latest data and insight means banks can engage with customers in real-time, offering contextual and timely support and personalised product recommendations.

5. Workflow automation

Augmenting CRM environments with advanced intelligence is helping banks automate routine tasks, such as research and data entry. This frees up more time for front line sales and marketing teams to focus more on complex, high-value customer interactions.

6. Enhances regulatory compliance

CRM has a key role to play in helping banks maintain compliance with regulatory requirements. A meticulous, up-to-date record of customer data is vital for KYC and AML compliance, as well as adherence with legislation such as the Consumer Duty. Being able to analyse overall exposures across an entire client base also empowers banks to work more proactively in spotting trends and emerging risks.

7. Environmental, Social and Governance considerations

Access to a wide range of data sources collated within the CRM environment can helps banks track how they are meeting their ESG goals. Enhancing their ability to not only align their products and services with the ESG concerns of customers, but also ensuring that customers meet their own ESG appetites and risk thresholds.

Augment your CRM environment with FullCircl

FullCircl can be integrated effortlessly via API into any CRM environment – Salesforce, HubSpot, Microsoft Dynamics and so on, to deliver a seamless data exchange with real-time risk analytics, and decisioning metrics from the widest range of industry leading global data sources.

With access to data on over 5 million companies in the UK, you can:

• Identify high-potential leads
• Build stronger, more personalised connections
• Automatically keep informed on key customer activities, ensuring you never miss an opportunity to nurture and grow client relationships.
• Utilise contextual insights to drive next best actions
• Stay ahead of the competition with actionable insights
• Access tools that transform your approach to relationship management, driving retention and repeatable business growth.

Take, for example NatWest. By integrating FullCircl into its Microsoft Dynamics environment it’s frontline teams have access to deeper insights into customer behaviours, needs and pain points, as well as any potential risks they pose in terms of KYC and AML compliance. This allows for more personalised interactions and more informed decision-making.  With FullCircl seamlessly integrated into the Dynamic interface, NatWest’s users do not need to switch between different systems to access the information they need, thereby saving time, improving the user experience whilst helping boost revenue growth.

With FullCircl integrated into your CRM environment you’ll:

• Increase your finance ready leads by 300%
• Get a 55% higher return on your CRM investment
• Activate the ability to onboard customers as much as 94% faster

Ready to supercharge your CRM investment in 2025?  Book a demo now

In the dynamic world of online gambling, fraud is an ever-present challenge that evolves alongside technological advancements. FullCircl recently spoke with experts at Sift to delve into the complexities of fraud in gambling, with a focus on the critical period following account opening. From account takeovers to bonus abuse, the conversation shed light on the tactics fraudsters employ and the emerging trends that operators need to counteract.

Fraudulent behaviour in gambling often intensifies after account opening. What are the primary types of post-account-opening fraud that gambling operators face today, and what trends do you see emerging in these attacks?

The primary types of post-account-opening fraud that gambling operators face today include:

• Account Takeover (ATO): Compromised accounts are a significant issue in the online gambling industry. Fraudsters gain unauthorised access to user accounts, often using stolen credentials. They then change account details, such as passwords and contact information, link new deposit accounts, and engage in suspicious spending. Behavioural analytics can help identify these changes and alert operators to potential takeovers.

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• Bonus Abuse and Promotion Fraud: Fraudsters exploit promotional offers and bonuses by creating multiple accounts to maximise their gains. This type of fraud is prevalent as it allows bad actors to benefit from incentives meant for trusted users. Identifying and preventing this requires monitoring for patterns such as the use of a single device for multiple accounts or geolocation discrepancies.

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• ‍First-Party Fraud: Also known as friendly fraud, first-party fraud occurs when legitimate users deny transactions or bets they have placed, often to get refunds or avoid losses. Managing these cases involves careful analysis of transaction histories and user behaviour to distinguish between legitimate disputes and fraudulent claims.

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• Collusion and Syndicate Betting: Fraudsters collaborate to manipulate betting outcomes, often by placing bets on different accounts to cover all possible outcomes. Detecting these patterns requires sophisticated analysis of betting behaviours and connections between accounts.

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• AI-Driven Tactics: More fraudsters are experimenting with leveraging AI to refine their tactics, making it more challenging for traditional detection methods to detect abuse. The democratisation of fraud tools, such as those available on platforms like Telegram, has made it easier for even amateur fraudsters to engage in these activities.

To combat these evolving threats, gambling operators need to leverage advanced fraud prevention technologies that provide real-time monitoring, behavioural analytics, and comprehensive risk assessments. Working with industry experts and continuously updating fraud detection strategies are also crucial in staying ahead of these sophisticated attacks.

Account takeover is a major issue in the gambling industry. How can operators effectively identify and respond to suspicious behaviours without compromising the user experience for legitimate players?

One approach to solving account takeovers with accurate, low-friction processes is to lean on holistic passive datasets, including device intelligence, geolocation, and behavioural analytics. Each example dataset can be tracked during any point across a user session and provide valuable information to analyst teams.

A common process includes the following:

• Leverage device intelligence to consider a list of trusted devices and identify whether a new device is currently being used with the account.
• Geolocation might help identify that this suspicious activity is from a geolocation far away from the typical location(s) of trusted devices.
• Behavioural analytics has the potential to show analysts that the user changed account details, such as the password, contact information change, linking of new deposit accounts, and subsequent suspicious spending.

From a customer satisfaction perspective, user data patterns can be used to eliminate the need to apply additional friction to trusted customers. From a fraud prevention perspective, it becomes much easier to spot suspicious activity by monitoring account activity.

One, or several, passive datasets can be employed in any combination of ways. For the best results, it’s recommended to work with fraud experts not only to define solutions for the challenges of today, but to lay the groundwork required for your company to respond nimbly to future challenges.

With the rise of “bonus abuse” and “promotion fraud,” what strategies can gambling operators use to balance user acquisition through incentives while protecting themselves against exploitation?

The majority of users seeking to exploit promotion and bonus systems rely on multiple accounts to make it worth their while. Trusted players and non-abusers typically maintain a single account on a platform.

Identifying bad actors requires the use of data to determine suspicious activity:

• “First Seen” users can be an intimidating subset of accounts to evaluate due to the lack of information available. Typically, this results in the use of ‘rule-based’ processes, which are not informed enough to perform well, are hard to maintain, and fail even more as the platform scales. By partnering with a fraud prevention vendor, your platform can benefit from the use of network data, which provides insight into the performance of the users across the industry to effectively neutralise the idea of “first seen” users.
• Address information can serve to show platforms that a single address is used for a high number of accounts. This can also be applied to phone numbers.
• Email addresses are known to be manipulated by a single user to create numerous accounts. An example is J.DOE@XYZ.com being turned into JD.OE@XYZ.com. Systems will see that this is a new email address and allow for the user account to be created.
• Device fingerprinting is a highly-viable dataset to deploy. A single device linked to numerous accounts should raise flags for analyst teams.  
• Geolocation that’s far removed from registered addresses might help identify when a bad actor is operating with stolen information.

By employing these datasets, analyst teams are empowered to get the most insight, driving up accurate decisioning without forcing trusted players through additional friction.  

How do gambling operators manage “friendly fraud” cases, such as users denying transactions or bets they’ve placed, and what are the latest trends in managing these types of claims?

Gambling operators manage friendly fraud cases, such as users denying transactions or bets they’ve placed, by leveraging advanced fraud detection tools and strategies. These include analysing transaction histories and user behaviour to distinguish between genuine disputes and fraudulent claims. Operators often use behavioural analytics to identify patterns that indicate friendly fraud, such as frequent chargebacks or disputes from the same user. The latest trends in managing these types of claims involve the use of AI and machine learning to detect anomalies and predict potential fraud before it occurs. Additionally, operators may implement more stringent verification processes to ensure that transactions are legitimate.

Collusion and syndicate betting pose unique risks to gambling operators. What are some of the best ways to detect patterns of collusion between accounts, especially as fraudsters become more sophisticated in masking their activities?

To detect patterns of collusion between accounts, gambling operators use sophisticated analysis of betting behaviours and connections between accounts. This includes monitoring for unusual betting patterns, such as multiple accounts placing bets on all possible outcomes of an event. Operators also utilise advanced multi-account detection systems that analyse various signals, including account details, payment data, and device information, to uncover potential links between accounts. Real-time monitoring and AI-powered risk scoring are also employed to assess risk based on player behaviour and quickly identify suspicious activities.

As fraud techniques in gambling continue to become more complex, what are some effective ways for operators to account for regional differences in fraud patterns, payment methods, and regulatory requirements?

As fraud techniques in online gambling become more complex, operators account for regional differences in fraud patterns, payment methods, and regulatory requirements by customising their fraud prevention strategies to fit the specific needs of each region. This involves understanding the unique fraud trends and regulatory landscapes of different regions and adapting their detection methods accordingly. Operators may also work with local experts and leverage region-specific data to enhance their fraud prevention efforts.

What data signals or user behaviours are most predictive of post-account-opening fraud in the gambling industry, and how can operators prioritise these without creating false positives?

The most predictive data signals and user behaviours for post-account-opening fraud in the gambling industry include unusual payment behaviours, high-velocity transactions, and the use of multiple payment methods. Operators prioritise these signals by employing AI and machine learning models that can differentiate between legitimate high-value players and potential fraudsters. This helps minimise false positives while ensuring that genuine users are not subjected to unnecessary friction.

In the fast-paced environment of online gambling, how can operators detect and respond to fraud in real time, particularly when high-volume events and live betting add pressure to systems and teams?

Operators detect and respond to fraud in real time by using advanced fraud prevention technologies that provide real-time monitoring and behavioural analytics. These systems can quickly identify and flag suspicious activities, allowing operators to take immediate action. During high-volume events and live betting, operators rely on automated systems and AI-driven risk scoring to manage the increased pressure on their systems and teams.

How are gambling operators addressing the challenge of high-risk accounts with unusual spending or withdrawal patterns, and what measures can be taken to minimise financial loss while avoiding alienating genuine users?

Gambling operators address the challenge of high-risk accounts with unusual spending or withdrawal patterns by implementing measures such as enhanced verification processes and continuous monitoring of account activities. These include access from new devices or geolocations, changing of account contact information, changing of linked accounts (for deposit or withdrawals), and more.

They use AI and machine learning to identify and flag high-risk behaviours, allowing them to take proactive steps to minimise financial loss while avoiding alienating trusted players. Operators may also offer personalised support to high-value players to ensure a positive user experience.

Given the growing regulatory scrutiny in the gambling industry, what challenges do operators face in balancing regulatory compliance with fraud prevention, especially as anti-money laundering (AML) and KYC requirements become stricter?

Given the growing regulatory scrutiny in the gambling industry, operators face challenges in balancing regulatory compliance with fraud prevention. This includes adhering to strict anti-money laundering (AML) and Know Your Customer (KYC) requirements while maintaining effective fraud detection systems. Operators address these challenges by integrating compliance checks into their fraud prevention platforms and using AI-driven systems to ensure ongoing compliance with regulations. This approach helps operators meet regulatory requirements while protecting their platforms from fraud.

We sometimes hear of gambling operators “accepting the cost of fraud” – How does Sift support these operators to empower a more proactive approach to fraud prevention?

When fraud prevention providers initially carved out their own industry, it was reasonable to maintain that fraud was a cost of doing business. The risk of insulting trustworthy users outweighed the benefit of catching bad actors at a less-than-profitable level. This was due, in large part, to the stunted availability of actionable data and supporting technology. Times have changed. For 13 years, Sift has worked in tandem with some of the most notable platforms worldwide to build a robust network of data. This data supports the machine learning models we have worked with since our inception.

Sift empowers businesses with consistent innovation, new datasets, and advanced functionality, leading to accurate and timely decision-making. For teams aiming to transition from reactive to proactive fraud prevention, Sift offers three primary benefits:

• Clearbox Decisioning: No one knows your business better than you. Network models perform well, but have limitations. By empowering your team with data and technology, your platform can perform at its best. More businesses are seeking transparency and control with their processes and are looking to prepare for future AI/ML regulations.

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• Efficiency Solutions: Reduce manual review by leveraging simple (or robust) workflows. Trigger events from any point along your customer journey and build cases with more information.

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• Industry Expertise: Instead of waiting for the attacks to reach your front door, identify emerging industry trends proactively by working with Sift’s Trust and Safety Architects (TASAs) and build workflows proactively.

Explore how to prevent post-account opening fraud with Sift here.

Environmental, Social and Governance (ESG) data and insight is fast becoming an essential factor in improving underwriting profitability.

Utilising ESG data can enhance risk assessments, improve loss ratios, product and wording innovation, and reduce carrier exposures. It’s also increasingly vital to meeting regulatory governance requirements.

However, according to a Capgemini report, relatively few insurers have begun factoring sustainability into their underwriting practices. In fact, fewer than half of property and casualty insurers embed ESG scores in the underwriting process.  

This is reiterated by Marsh, who report that only 25% of insurers are developing group-wide underwriting frameworks or guidelines across their portfolio that take either climate and/or sustainability into account.

This is concerning. The risks from global warming and severe weather conditions are exponentially increasing commercial property exposures. On the liability side we’re seeing an increasing number of ESG-related lawsuits against directors and officers.  Rather then being behind the curve, underwriters should in fact be on the front line of ESG transformation.

Why the reticence?

It would be fair to assume that traditional underwriting approaches are not sufficient to identify and manage the complex risks presented by incorporating ESG into decisioning. However, perhaps the biggest barrier is the persistent challenge insurers face in capturing ESG data and embedding it in their underwriting processes. Insurers face huge challenges to accessing reliable ESG data on small and private companies.

But the fact remains, underwriters assessing price and risk in an ever-evolving landscape need a clear ESG underwriting framework, and realising this vision requires them to incorporate richer data from a wide variety of sources.

It’s vital that insurers provide a clear and enhanced understanding of ESG risks within their underwriting portfolios. By incorporating ESG data insights with underwriting expertise, insurers can further advance underwriting decisioning and ensure the sustained profitability of portfolios moving forward.

The time to make underwriting more sustainable is now

Investment in an integrated view of company level ESG risks and opportunities, as part of a wider holistic single customer view, allows underwriters to understand emerging, complex, and interconnected risks and incorporate them into decision making.  

This illustrates an innovative approach and commitment to delivering the most sophisticated underwriting decision-making for improved policyholder, broker and carrier outcomes.  

ESG risks will only continue to grow.  Those that invest now in surfacing and integrating data from the widest possible range of sources will get ahead of the game when it comes to the future of dynamic risk selection, pricing and portfolio management.

The three biggest benefits of incorporating ESG data into underwriting

Enhanced risk decisioning

ESG data can help underwriters evaluate commercial exposures to environmental risks. For example, damage to property and potential business interruption risks due to extreme weather events, pollution etc.  

Likewise, insights into labour practices, community impact, supply chain vulnerabilities, workplace culture, and manufacturing processes inform underwriters of the potential for financial and legal risks, such as greenwashing, mismanagement, insolvency, D&O claims and so on.

Predictive modelling and portfolio resilience

Integrating ESG metrics into actuarial modelling can deliver refined risk predictions that minimise carrier exposures to catastrophic losses, business interruptions, liability claims exposures and so on.

ESG data and insights as part of a holistic underwriting strategy can also help insurers build more resilient portfolios, by avoiding over exposure to high-risk clients and instead pinpointing business with strong ESG performance and therefore lower risk presentations.

Businesses with higher ESG scores often exhibit reduced volatility, thanks to better cultures, safer workplaces, sustainability targets, and ethical supply chains, which also serves to improve portfolio performance. 63% of insurers say that a positive ESG profile will likely affect an insured’s underwriting outcomes, and of that group 80% believe a positive ESG profile can lead to increased insurance capacity for the insured.

Loss ratios and claim reduction

ESG data can help reduce loss ratios through improved risk selection and more responsive policy wordings.  

There is causation and correlation evidence between ESG performance, risk information and loss ratios. For example, a company with strong employee health and safety practice is typically a lower employer’s liability risk.

ESG data also offers insurers the opportunity to play a more risk advisory role, thereby delivering the prevention solutions that all types and sizes of businesses need.  This in turn translates into fewer claims.

Discover a better way to orchestrate ESG data into underwriting

Just as ESG data has become a huge factor in commercial banking decision-making, so it will also become more prevalent in underwriting practices, both in terms of building resilience against emerging risks and realising the opportunities presented by a more ethical and sustainable world.  

There’s huge potential to use ESG data in positive ways. Not only to better triage, assess, and price risks, but to boost resilience from ESG threats, and help insurers play their part in facilitating the transition to a greener economy.  

For ESG data to be effectively woven into existing underwriting processes, insurers must adjust their data architecture and redefine a data collection and analysis strategy.

Investing in a single orchestration platform that integrates ESG data (including CSR reports, sustainability reports, ratings etc) with a wide range of financial data (annual reports, credit data, Companies House filings etc,) and risk information (organisational structure, PEPs and sanctions) for a single source of truth is essential if insurers are to effectively integrate ESG consideration into underwriting decision-making.  

In addition, integrating real-time single view of the client risk visibility, data-driven workflows, and predictive analytics into underwriting workbenches, actuarial tools and pricing platforms holds many advantages, including stripping out the cost of duplication and lowering the risk of data inconsistency. Meanwhile, harnessing AI-powered rules-based decisioning over this single customer view can help underwriters triage presentations faster and respond in a more agile way to customers that meet underwriting appetite.

Learn how FullCircl can redefine your approach to ESG data orchestration and improve underwriting decisioning. Book a demo here.