Tracing Crime With Technology: The Future Of Investigations

Introduction

As per Kroll's Financial Crime Report 2025, over 70% of executives expect financial crime risk to increase, yet only 23% believe their organization's compliance program is very effective in combating it. The sophistication of financial crimes continues to grow, and criminals are employing innovative methods to avoid detection. Emerging technologies are essential for contemporary investigations because traditional forensic techniques are struggling to keep pace.

Big data analytics, blockchain, biometrics, and machine learning are examples of innovative solutions that give professionals the ability to track illegal activity, discover hidden patterns, and enhance security. By reducing losses and improving compliance, these advances enable organizations to move from reactive fraud detection and investigations to proactive risk mitigation. Forensic investigators can increase security and effectiveness in the battle against financial crimes by combining automated data analysis, predictive insights, and real-time monitoring. New technologies enhance the proactive approach to fraud detection and mitigation, considering the increasing sophistication of financial crimes.

Digital Deception: The Evolving Threat

What happens when the tools used to fight crime become the weapons used to commit it? In today's digital age, emerging technologies are our greatest defense and our newest threat. Of respondents surveyed as part of Kroll's Financial Crime Report 2025, 49% agreed that AI¹ poses a significant risk to financial crimes compliance. Deepfakes, synthetic identities, and automated phishing attacks are on the rise. Criminals are now using AI to replicate voices and forge documents. The tools designed to combat fraud are being co-opted to orchestrate it. The following are a few of the myriad ways criminals are using technology.

Document Tampering

Tech-driven document tampering, especially in regulatory, financial, and legal contexts, is an increasingly widespread risk. As tools become more advanced, they can be abused to create or change official documents, invoices, and contracts in ways that are difficult to discover. GenAI platforms coupled with resolute fraudsters who are learning how to use these tools to their maximum potential make it increasingly difficult for the average individual to spot minute discrepancies in documents at a cursory glance.

Impersonation Tactics

Dealing with a new generation of deception makes it difficult to distinguish between real and fake, from the use of fake accounts to deepfake voices that mimic CEOs and machine learning-created fake identities. Traditional defenses and controls are outdated and, in this changing environment, are outperformed.

The Financial Crimes Enforcement Network recently released an alert in the wake of evolving and emerging technologies to help financial institutions counter fraud schemes associated with deepfake media. The alert acknowledged that recent developments in GenAI tools have greatly reduced the resources required to produce high-quality synthetic content. The companies producing GenAI tools have committed to implementing oversight and controls intended to mitigate malicious deepfakes and other misuse. However, given the open-source nature of many of these platforms, criminals may develop alternate methods to evade the safeguards.

Synthetic Identity Generation

Because it is subtle, slow, and incredibly difficult to identify, synthetic identity fraud, in many respects, is the perfect crime. In contrast to traditional identity theft, which involves the theft of a real person's information, synthetic identities are created by piecing together bits of fiction and truth, such as a stolen address, a fake name, or a real identity number. The result is a new, fake person who is nonexistent, except in the eyes of financial systems, which are designed to believe data rather than challenge it. This creates a maze with no obvious starting point for forensic investigators. While remaining undetected, these phony profiles can apply for loans, establish credit, and even pass Know Your Customer checks.

Associated Risks

• Fraudulent Transactions: Falsified contracts, invoices, and financial statements facilitate financial fraud.
• IdentityTheft: People may be able to impersonate others or obtain unauthorized access to sensitive systems using forged identification documents and credentials.
• Corporate Espionage: People may use falsified contracts, employment records, and business records for competitive deception or infiltration.
• Regulatory and Compliance Risks: False documents can evade security checks and lead to legal infractions.
• Deepfake Manipulation: Modified signatures and photos make it harder to verify, which makes fraud possible.
• Money Laundering Risks: With the creation of synthetic identities, launderers can create an ever-expanding, confusing trail of funds to launder money across the globe.

How Technology Is Transforming Financial Investigations and Compliance Programs

The growth of emerging technologies led companies to recognize the benefits possible for their own compliance and investigation programs. Kroll's 2025 Global Business Sentiment Survey suggests that while business leaders view AI integration as a major opportunity for the coming year, many remain uncertain about how to effectively implement AI governance.

The survey also indicated that 25% of respondents had already implemented AI into their risk management and financial crime compliance programs, while a similar percentage of respondents were in the early stages of adoption.

For investigations, emerging technology enabled investigators to analyze large datasets, find hidden patterns, and stop financial crimes at a much greater pace than previously thought possible.

• Automated Document Review: By classifying and prioritizing documents, contemporary tools minimize manual labor and expedite case preparation.
• Automated Text Analysis: Questionable changes are identified in financial statements and contracts.
• Effective Data Sorting: By using sophisticated technology to classify vast amounts of files according to metadata and content, investigators can concentrate on pertinent information, like potentially suspicious email exchanges, without having to go through an excessive amount of benign data by hand.
• Finding Hidden Patterns: Forensic tools help investigators find important connections by examining user activity and timestamps across several systems to find anomalies that might otherwise go overlooked, like file deletions that occur right after unauthorized logins.
• Natural Language Processing (NLP): Tools that use NLP can comprehend the meaning and context of legal documents, which improves the accuracy of searches.
• Handwriting Analysis: Advanced systems that analyze handwriting characteristics for fraud detection, identification, and authentication produce a comprehensive profile of a person's handwriting. These systems analyze certain components like stroke patterns, letter spacing, pen pressure, and angular consistency, improving the precision of security and verification procedures.
• Predictive Coding: Machine learning algorithms are used to find pertinent documents based on previous review trends, allowing accuracy and consistency to increase.
• Advanced Pattern Analysis: To identify transactional patterns within larger financial activities, certain forensic systems make use of probabilistic models and complex sequence analysis. For instance, in round-tripping fraud, products are first sold but then covertly repurchased, thereby inflating reported revenue.
• Facial Recognition and Image Analysis: Facial recognition analyzes individuals through their unique facial features, which are converted into digital faceprints, whereas image analysis involves interpreting visual data to detect patterns, objects, or anomalies. Both rely on machine learning and computer vision technologies to deliver accurate and actionable insights.
• Generative Summarization: Simplifies review procedures by producing succinct summaries of lengthy legal documents.

Fighting Crime with Emerging Technology

The impact and credibility of using emerging technology in preventing and detecting crime in India and globally are demonstrated by the following real world examples. These cases demonstrate how emerging technologies are supporting and fundamentally changing the way crimes are detected and deterred.

Govern with Confidence: Best Practices for Emerging Tech

While emerging technologies will support tackling crime, organizations should adhere to best practices to successfully incorporate innovative technologies into financial investigations.

• Creating Policies and Guidelines: Build governance frameworks to regulate the risks from the use of emerging technologies. Governing bodies should be created within organizations to regulate the use of these technologies.
• Checking AI for Ethics and Bias: Conduct ethics and bias assessments to ensure compliance with evolving AI regulations. These assessments help organizations identify and mitigate risks related to fairness, transparency, and accountability in AI systems, while aligning with global standards for responsible AI deployment.
• Complying with Data Security and Privacy: Compliance may be necessary for personal data processing activities that risk conflicting with India's Digital Personal Data Protection Act, global frameworks like the EU's GDPR, and others, given their differing requirements on consent, data categorization, and cross-border data flows.
• Balancing Cost and Benefits: Evaluate the financial outlay for new technology concerning gains in productivity and fraud detection.
• Training and Development: To give teams the know-how in data analytics, forensic investigation, and emerging technologies to ensure smooth tech adoption. Design specialized training programs to support governance, compliance, and responsible usage.
• Adapting to Changing Fraud Tactics: As financial criminals create new ways to circumvent technological advancements, it is important to continuously improve detection techniques.

In this technology-enabled business environment, AI is a powerful tool that enhances the speed and accuracy of investigations. It helps analyze large volumes of data efficiently. It is being used in areas such as handwriting analysis, electronic data reviews, language translation, sentiment assessment, metadata examination, video and image investigation, big data analytics, and financial record reviews. These applications are helping professionals uncover insights faster and with greater precision.

Without structured oversight, businesses risk exposure to unintended biases, regulatory violations, and security vulnerabilities. Kroll provides expert advisory services to help companies responsibly manage AI-driven transformation.

Conclusion

It is now imperative—not optional—to incorporate emerging technologies into investigations as financial crimes become more complex.

Footnote

1. A technology that enables computers and machines to simulate human learning, comprehension, problem solving, decision-making, creativity, and autonomy.

The content of this article is intended to provide a general guide to the subject matter. Specialist advice should be sought about your specific circumstances.