
Intelligence-led economic crime & security solutions
Intelligence-led economic crime & security solutions
Economic criminals continue to outpace the intelligence designed to stop it.
Economic criminals continue to outpace the intelligence designed to stop it.
Financial, legal and security services are operating with compliance tools built for a different era - generating false positives at 95%, missing the threats that matter, and carrying the regulatory cost of inadequate controls. There is a better way.
Financial, legal and security services are operating with compliance tools built for a different era - generating false positives at 95%, missing the threats that matter, and carrying the regulatory cost of inadequate controls. There is a better way.

Intelligence-led economic crime & security solutions
Economic criminals continue to outpace the intelligence designed to stop it.
Financial, legal and security services are operating with compliance tools built for a different era - generating false positives at 95%, missing the threats that matter, and carrying the regulatory cost of inadequate controls. There is a better way.
$3.1 Trillion
$3.1 Trillion
Estimated annual
value of global
money laundering
Estimated annual value of global
money laundering
UNODC World Drug
Report 2023
UNODC World Drug
Report 2023
95%+
95%+
False positive rate in rule-based transaction monitoring systems
False positive rate in rule-based transaction monitoring systems
Wolfsberg Group AML Compliance Cost Benchmark 2024
Wolfsberg Group AML Compliance Cost Benchmark 2024
€180 Billion
€180 Billion
Lost annually
to compliance inefficiency in banking
Lost annually to compliance inefficiency in banking
Industry modelling, Elemental Intelligence business case 2026
Industry modelling, Elemental Intelligence business case 2026
12–18 Months
12–18 Months
Typical implementation timeline before first meaningful output
Typical implementation timeline before first meaningful output
Palantir implementation benchmarks; Redshift Apex analysis 2026
Palantir implementation benchmarks; Redshift Apex analysis 2026
The problem is systemic. The tools are not.
The AI promise is not delivering.
The problem is systemic. The tools are not.
The AI promise is not delivering.
The problem is systemic. The tools are not.
The AI promise is not delivering.
Major institutions are facing a crime and intelligence threat that has evolved faster than the systems designed to detect it.
Major institutions are facing a crime and intelligence threat that has evolved faster than the systems designed to detect it.
The scale of the threat
The scale of the threat
The scale of the threat
Economic crime has evolved from opportunistic fraud into coordinated, state-sponsored networks that deliberately exploit the blind spots of global finance. For commodity trading groups, shadow fleets evade sanctions through ship-to-ship transfers and flag changes invisible to standard counterparty screening, while trade-based money laundering moves billions through pricing manipulation that payment-level monitoring cannot detect. For global commercial banks, the same actors exploit correspondent banking networks to layer illicit funds across jurisdictions, construct beneficial ownership structures that defeat standard KYC, and conceal criminal flows within the sheer volume of legitimate transactions. For security services, the financial networks funding hostile states, proliferation programmes, and organised crime are increasingly indistinguishable from legitimate commercial activity — threading through the same commodity trades, the same correspondent banking channels, and the same encrypted communications platforms that markets depend on. Market abuse operates across all three through trading communications that keyword-based surveillance cannot parse. The threat is systemic, adaptive, and state-sponsored. The response has not kept pace.
The compliance failure
The compliance failure
The compliance failure
Most financial institutions are running compliance functions that generate hundreds of thousands of alerts — of which 95% or more are false positives. Analysts spend their careers investigating what does not exist while the real-time threats accumulate undetected. This is not surveillance. It is noise. The compliance function has become a cost centre that consumes resource without delivering proportionate risk reduction, and the structural cause — rule-based, static monitoring tools — cannot be resolved by adding more rules or more headcount.
The regulatory ratchet
The regulatory ratchet
The regulatory ratchet
Regulators are no longer accepting systemic compliance failure as an operational reality. OFAC recorded record industry-wide penalties of $4.3 billion in 2025. The Economic Crime and Corporate Transparency Act 2023 introduced corporate criminal liability for failure to prevent fraud — with no requirement to prove senior management knowledge. The FCA's 2025 AI guidance signals that automated, explainable detection systems are now expected in high-risk sectors. The direction is clear: inadequate controls carry personal accountability, not just institutional penalties.

AI was supposed to solve this. For most organisations, it hasn’t.
90% of AI pilots fail to scale beyond process automation. Leadership confidence in AI delivery is falling. The hype has not been matched by results — and the reasons are structural, not circumstantial.
DATA ACCESS, NOT PROBLEM-SOLVING
Major established platforms make data easier to access.
They do not make problems easier to solve.
Organising information is not the same as creating intelligence and insight. You still need to know what question to ask — and the answer to 'what do we do with this data and how do we act on it' is not in any current analytics platform. The critical strategic question remains unanswered after a typical implementation of 12 to 18 months and €5–15 million in investment. The competitive frontier for companies is cognition — the answer is context and have the right engine to solve it.
TACTICAL PILOTS THAT DO NOT SCALE
90% of AI pilots have failed to scale beyond process automation. The reasons are structural and experiential, not technical.
Generic AI tools are not built for the specific data models, regulatory frameworks, audit / evidencing requirements and detection typologies of economic crime, let alone delivering the supporting change that enables action. They require years of configuration and fine-tuning to approach required accuracy levels. They also hallucinate generating confident, plausible, and wrong outputs that cannot be tolerated in regulatory environments. And they cannot meet the explainability standards that the FCA and OFAC now expect.
Agentic AI projects are failing throughout fintech when the agent is directed to work with very large or very siloed data, as the agents do not have the ability to understand what such complex data genuinely means.
Leadership confidence in AI is falling precisely because the hype has not been matched by delivery, whilst the need to reduce costs and improve effectiveness are only increasing, along with the unmet vision to create new insights and competitive value from data.
VENDOR DEPENDENCY, NOT CLIENT CAPABILITY
The current model of AI deployment creates dependency on third party advisors and technologists who do not understand the business, are not seeking to build enduring capability or knowledge within the perimeter. Clients end up with neither the solution nor the understanding on how to maintain and improve, further undermining the operational resilience of the world's biggest organisations. Agents, Data and learning must sit and evolve inside the business.
Pilots become the vendor's learning exercise. Embedded consultants absorb institutional knowledge. Internal teams are bypassed rather than developed. When the engagement ends — and they always end — the client is more reliant on external vendors and less capable of independent AI adoption than when they started. This is not a side effect of the current model. It is the model. And it is the primary reason that AI investment in financial services continues to generate strategic disappointment despite significant capital commitment.
AI was supposed to solve this. For most organisations, it hasn’t.
90% of AI pilots fail to scale beyond process automation. Leadership confidence in AI delivery is falling. The hype has not been matched by results — and the reasons are structural, not circumstantial.
DATA ACCESS, NOT PROBLEM-SOLVING
Major established platforms make data easier to access.
They do not make problems easier to solve.
Organising information is not the same as creating intelligence and insight. You still need to know what question to ask — and the answer to 'what do we do with this data and how do we act on it' is not in any current analytics platform. The critical strategic question remains unanswered after a typical implementation of 12 to 18 months and €5–15 million in investment. The competitive frontier for companies is cognition — the answer is context and have the right engine to solve it.
TACTICAL PILOTS THAT DO NOT SCALE
90% of AI pilots have failed to scale beyond process automation. The reasons are structural and experiential, not technical.
Generic AI tools are not built for the specific data models, regulatory frameworks, audit / evidencing requirements and detection typologies of economic crime, let alone delivering the supporting change that enables action. They require years of configuration and fine-tuning to approach required accuracy levels. They also hallucinate generating confident, plausible, and wrong outputs that cannot be tolerated in regulatory environments. And they cannot meet the explainability standards that the FCA and OFAC now expect.
Agentic AI projects are failing throughout fintech when the agent is directed to work with very large or very siloed data, as the agents do not have the ability to understand what such complex data genuinely means.
Leadership confidence in AI is falling precisely because the hype has not been matched by delivery, whilst the need to reduce costs and improve effectiveness are only increasing, along with the unmet vision to create new insights and competitive value from data.
VENDOR DEPENDENCY, NOT CLIENT CAPABILITY
The current model of AI deployment creates dependency on third party advisors and technologists who do not understand the business, are not seeking to build enduring capability or knowledge within the perimeter. Clients end up with neither the solution nor the understanding on how to maintain and improve, further undermining the operational resilience of the world's biggest organisations. Agents, Data and learning must sit and evolve inside the business.
Pilots become the vendor's learning exercise. Embedded consultants absorb institutional knowledge. Internal teams are bypassed rather than developed. When the engagement ends — and they always end — the client is more reliant on external vendors and less capable of independent AI adoption than when they started. This is not a side effect of the current model. It is the model. And it is the primary reason that AI investment in financial services continues to generate strategic disappointment despite significant capital commitment.

AI was supposed to solve this. For most organisations, it hasn’t.
90% of AI pilots fail to scale beyond process automation. Leadership confidence in AI delivery is falling. The hype has not been matched by results — and the reasons are structural, not circumstantial.
DATA ACCESS, NOT PROBLEM-SOLVING
Major established platforms make data easier to access. They do not make problems easier to solve.
Organising information is not the same as creating intelligence and insight. You still need to know what question to ask — and the answer to 'what do we do with this data and how do we act on it' is not in any current analytics platform. The critical strategic question remains unanswered after a typical implementation of 12 to 18 months and €5–15 million in investment. The competitive frontier for companies is cognition — the answer is context and have the right engine to solve it.
TACTICAL PILOTS THAT DO NOT SCALE
90% of AI pilots have failed to scale beyond process automation. The reasons are structural and experiential, not technical.
Generic AI tools are not built for the specific data models, regulatory frameworks, audit / evidencing requirements and detection typologies of economic crime, let alone delivering the supporting change that enables action. They require years of configuration and fine-tuning to approach required accuracy levels. They also hallucinate generating confident, plausible, and wrong outputs that cannot be tolerated in regulatory environments. And they cannot meet the explainability standards that the FCA and OFAC now expect. Agentic AI projects are failing throughout fintech when the agent is directed to work with very large or very siloed data, as the agents do not have the ability to understand what such complex data genuinely means. Leadership confidence in AI is falling precisely because the hype has not been matched by delivery, whilst the need to reduce costs and improve effectiveness are only increasing, along with the unmet vision to create new insights and competitive value from data.
VENDOR DEPENDENCY, NOT CLIENT CAPABILITY
The current model of AI deployment creates dependency on third party advisors and technologists who do not understand the business, are not seeking to build enduring capability or knowledge within the perimeter. Clients end up with neither the solution nor the understanding on how to maintain and improve, further undermining the operational resilience of the world's biggest organisations. Agents, Data and learning must sit and evolve inside the business.
Pilots become the vendor's learning exercise. Embedded consultants absorb institutional knowledge. Internal teams are bypassed rather than developed. When the engagement ends — and they always end — the client is more reliant on external vendors and less capable of independent AI adoption than when they started. This is not a side effect of the current model. It is the model. And it is the primary reason that AI investment in financial services continues to generate strategic disappointment despite significant capital commitment.
What the problem actually requires.
What the problem actually requires.
What the problem actually requires.
The failures of existing platforms are not failures of intent, effort or investment by clients. They are failures of design, problem solving, approach and experience. The right answer requires a different kind of solution — and a different kind of partner.
The failures of existing platforms are not failures of intent, effort or investment by clients. They are failures of design, problem solving, approach and experience. The right answer requires a different kind of solution — and a different kind of partner.
DOMAIN-NATIVE INTELLIGENCE
Precision:
Intelligence built for the specific problem — not adapted from somewhere else.
Optimised AI adapted to your needs and context, co-created, building capability built from the ground up around risk and intelligence typologies specific to your business and operating model, covering transaction data, customer networks, ownership models and third-party supply chains. Internal and external compliance intelligence should be connected within the platform — not configured from scratch at the client's expense over 18 months.
Optimised AI adapted to your needs and context, co-created, building capability built from the ground up around risk and intelligence typologies specific to your business and operating model, covering transaction data, customer networks, ownership models and third-party supply chains. Internal and external compliance intelligence should be connected within the platform — not configured from scratch at the client's expense over 18 months.
REGULATORY DEFENSIBILITY BY ARCHITECTURE
Proof:
Every output must be auditable and explainable to a regulator. This cannot be added after the fact.
SHAP-value attribution in plain English, regulatory typology citations, model cards, and tamper-evident audit trails must be built into the architecture - not retrofitted as compliance features. The FCA's 2025 AI guidance and OFAC's enforcement posture make clear that 'our AI said so' is not a defensible compliance rationale. The standard regulators are moving towards enforcing requires that every alert can be traced to its evidence and explained to a non-technical examiner without reference to model internals.
CAPABILITY TRANSFER, NOT VENDOR DEPENDENCY
Partnership:
A partner that builds your understanding, capability, cross-functional working and robust business case. Not one that builds their own.
The deployment model that creates lasting value is not a vendor relationship. It is a partnership that co-develops solutions, builds intelligence, leverages the breadth of experience from both teams, builds internal capability, and leaves the client more capable - not more dependent - at the end of each engagement. This needs outcome-focused domain experts who are accountable for results and for the growth of client capability, not for billable hours or the perpetuation of their own engagement.
The deployment model that creates lasting value is not a vendor relationship. It is a partnership that co-develops solutions, builds intelligence, leverages the breadth of experience from both teams, builds internal capability, and leaves the client more capable - not more dependent - at the end of each engagement. This needs outcome-focused domain experts who are accountable for results and for the growth of client capability, not for billable hours or the perpetuation of their own
engagement.

THE SOLUTION
Elemental Intelligence. Powered by Lovelace.ai.
We deploy the Elemental platform — Lovelace.ai's enterprise context engine, the only platform built specifically for autonomous AI agents operating in mission-critical environments — with a compliance intelligence layer purpose-built for the true risks organisations face. Not adapted from somewhere else. Built from here.
Lovelace was founded to solve this problem and has a track record of making big enterprise AI projects deliver. Their solution is Graph-based Context Engines, which are a translation layer between big foundation models (Claude, Gemini, OpenAI) and vast data, keeping costs practical even when the agent is reasoning about millions of events. Lovelace’s technology has recently been shown to perform at the level of Google Gemini’s Deep Financial Research, but at 1% of the compute cost and 3 times the speed.
Lovelace is run by Dr. Andrew Moore, a renowned British American computer scientist and one of the world’s foremost authorities on artificial intelligence. Moore was the former head of Google Cloud AI, dean of Carnegie Mellon’s School of Computer Science, and the first AI advisor to U.S. CENTCOM. Lovelace’s team includes the senior engineers who built HSBC’s first AI AML solution.
Financial Crime
Detection
Military & Security
Intelligence
Enhanced Risk
Management
Strategic
Problem-Solving
THE SOLUTION
Elemental Intelligence. Powered by Lovelace.ai.
We deploy the Elemental platform — Lovelace.ai's enterprise context engine, the only platform built specifically for autonomous AI agents operating in mission-critical environments — with a compliance intelligence layer purpose-built for the true risks organisations face. Not adapted from somewhere else. Built from here.
Lovelace was founded to solve this problem and has a track record of making big enterprise AI projects deliver. Their solution is Graph-based Context Engines, which are a translation layer between big foundation models (Claude, Gemini, OpenAI) and vast data, keeping costs practical even when the agent is reasoning about millions of events. Lovelace’s technology has recently been shown to perform at the level of Google Gemini’s Deep Financial Research, but at 1% of the compute cost and 3 times the speed.
Lovelace is run by Dr. Andrew Moore, a renowned British American computer scientist and one of the world’s foremost authorities on artificial intelligence. Moore was the former head of Google Cloud AI, dean of Carnegie Mellon’s School of Computer Science, and the first AI advisor to U.S. CENTCOM. Lovelace’s team includes the senior engineers who built HSBC’s first AI AML solution.
Financial Crime
Detection
Military & Security
Intelligence
Enhanced Risk
Management
Strategic
Problem-Solving

THE SOLUTION
Elemental Intelligence. Powered by Lovelace.ai.
We deploy the Elemental platform — Lovelace.ai's enterprise context engine, the only platform built specifically for autonomous AI agents operating in mission-critical environments — with a compliance intelligence layer purpose-built for the true risks organisations face. Not adapted from somewhere else. Built from here.
Lovelace was founded to solve this problem and has a track record of making big enterprise AI projects deliver. Their solution is Graph-based Context Engines, which are a translation layer between big foundation models (Claude, Gemini, OpenAI) and vast data, keeping costs practical even when the agent is reasoning about millions of events. Lovelace’s technology has recently been shown to perform at the level of Google Gemini’s Deep Financial Research, but at 1% of the compute cost and 3 times the speed.
Lovelace is run by Dr. Andrew Moore, a renowned British American computer scientist and one of the world’s foremost authorities on artificial intelligence. Moore was the former head of Google Cloud AI, dean of Carnegie Mellon’s School of Computer Science, and the first AI advisor to U.S. CENTCOM. Lovelace’s team includes the senior engineers who built HSBC’s first AI AML solution.
Financial Crime
Detection
Military & Security
Intelligence
Enhanced Risk
Management
Strategic
Problem-Solving
Built for the most complex evolving risk environments on earth.
Built for the most complex evolving risk environments on earth.
Built for the most complex evolving risk environments on earth.
Banking & Financial Services
Banking & Financial Services
Banking & Financial Services
Financial crime detection · Sanctions screening · Market manipulation surveillance · Regulatory reporting · Conduct risk monitoring
Financial crime detection · Sanctions screening · Market manipulation surveillance · Regulatory reporting · Conduct risk monitoring
Commodity Trading & Energy
Commodity Trading & Energy
Commodity Trading & Energy
TBML detection · Counterparty intelligence · Shadow fleet sanctions evasion · Market abuse surveillance · Third-party risk management
TBML detection · Counterparty intelligence · Shadow fleet sanctions evasion · Market abuse surveillance · Third-party risk management
Defence & Security Services
Defence & Security Services
Defence & Security Services
Strategic intelligence analysis · Threat assessment · Complex problem-solving · Supply chain interdiction · Mission-critical decision support
Strategic intelligence analysis · Threat assessment · Complex problem-solving · Supply chain interdiction · Mission-critical decision support
Law & Professional Services
Law & Professional Services
Law & Professional Services
Financial crime due diligence · M&A intelligence · High-consequence litigation · Regulatory exposure mapping · UBO analysis
Financial crime due diligence · M&A intelligence · High-consequence litigation · Regulatory exposure mapping · UBO analysis

The risk and compliance leaders of the next decade will be defined by decisions made now.
The risk and compliance leaders of the next decade will be defined by decisions made now.
The window in which AI-led intelligence represents genuine competitive and regulatory advantage is narrowing. We are ready to demonstrate working results within 90 days of data access agreement.
The window in which AI-led intelligence represents genuine competitive and regulatory advantage is narrowing. We are ready to demonstrate working results within 90 days of data access agreement.

The risk and compliance leaders of the next decade will be defined by decisions made now.
The window in which AI-led intelligence represents genuine competitive and regulatory advantage is narrowing. We are ready to demonstrate working results within 90 days of data access agreement.