Cybex Legal

Cybex MoneyTrail AI · for cyber-fraud & financial-crime cases

Trace the money.
Map the evidence.

Cybex MoneyTrail AI turns fragmented bank statements into a clear, source-linked money trail, so lawyers and investigators can see where the money went and empower legal judgment.

Request a Demo → See how it works
  • Every finding source-linked
  • Explainable by design
  • Human-controlled conclusions
  1. Transaction Data
  2. AI Intelligence
  3. Money Trail
  4. Evidence
  5. Legal Intelligence

The problem

The evidence is there.
The connections are not.

Financial investigations rarely fail for lack of data. They stall because the data is scattered across statements, accounts, companies and documents, and finding the critical path is still largely manual work.

Thousands of transactions can hide a small number of critical relationships.

Source material

  • Bank Statements
  • Ledgers
  • Invoices
  • FIR
  • Company Records
  • Property Documents

Reconstructed trail (illustrative)

  1. PersonPerson A
  2. AccountAccount 1
  3. AccountAccount 2
  4. CompanyCompany B
  5. CompanyCompany C
  6. AssetInvestment
  7. AssetProperty

The scale

Cyber fraud is a trillion-dollar problem

Every rupee lost to fraud moves through accounts. Tracing that movement quickly is often where recovery succeeds or fails.

Global scam losses, 12 months$1.03T

Only about 4% of victims recovered their losses.3

India cyber-fraud losses, 2024₹22,845 cr

Up 206% from ₹7,465 cr in 2023.4

India outlook, 2025 (estimate)₹1.2 lakh cr

I4C estimate of possible losses, about 0.7% of GDP.5

Global annual cost of cybercrime US$ trillion · 2015–20291, 2
51015EstimatedProjected$3T2015$6T2021$8T2023$9.5T2024$10.5T2025$11.36T2026$12.43T2027$13.82T2028$15.63T2029
  1. Cybersecurity Ventures — global cybercrime cost estimates, 2015–2025.
  2. Statista (as reported) — projections for 2026–2029. Projections are research-firm forecasts, not official data.
  3. Global Anti-Scam Alliance & Feedzai — Global State of Scams Report 2024.
  4. Ministry of Home Affairs, reply in Lok Sabha (July 2025) — India cyber-fraud losses, 2023–2024.
  5. Indian Cyber Crime Coordination Centre (I4C) — 2025 loss estimate.

Cybercrime-cost figures cover all damages, not only fraud losses.

The product · MVP

One focused job, done well

Turn fragmented financial records into a structured, navigable money trail. Where did the money come from? Where did it go? Who was involved? Which document supports each step?

  1. 01Upload
  2. 02Extract
  3. 03Normalize
  4. 04Connect
  5. 05Trace
  6. 06Visualize
  7. 07Report
  • Bank statement ingestion
  • Entity resolution
  • Financial chronology
  • Transaction extraction
  • Money-trail reconstruction
  • Pattern detection
  • Transaction normalization
  • Interactive transaction graph
  • Source-linked reports
  • Narrow MVP
  • Clear problem
  • Measurable value
  • Expandable architecture

Evidence traceability & architecture

Every finding has a source

The legal layer sits on top of a verified factual and transactional foundation. Each phase builds on the one before it.

  1. Phase 1Money Trail
  2. Phase 2Documents & Evidence
  3. Phase 3PMLA Legal Intelligence
  4. Phase 4Judgments & Precedents
  5. Phase 5Enterprise Financial-Crime Intelligence

Three layers, always kept separate

Illustrative example

AI AnalysisAI Finding₹18,00,000 moved from Account A to Account B
FactTransactionDate · Amount · UTR
FactSource DocumentBank Statement — Page 47
FactRelated Transaction₹16,50,000 → Entity C
AI AnalysisEvidence ReviewSupporting and contradictory evidence surfaced
LegalLegal IssueRequires professional assessment
Fact

What the data or document shows.

AI Analysis

What the system identifies or connects.

Legal Determination

Assessed by the legal professional or authority.

No black-box legal conclusions.

Responsible AI

AI analysis ≠ legal determination

  • 01

    Source-grounded

    Verified statutory and judicial sources.

  • 02

    Evidence-traceable

    Every finding connects to source material.

  • 03

    Law-versioned

    Relevant law is determined by date and context.

  • 04

    Explainable

    The system explains why something was flagged.

  • 05

    Contradiction-aware

    Supporting and contradictory evidence are surfaced.

  • 06

    Human-controlled

    Material legal conclusions require professional review.

The AI identifies what deserves attention; the professional determines what it means.

The guardrails

What Cybex AI will — and will not — do

We believe these limits make the product stronger, not weaker.

Will do

  • Identify transactions
  • Connect entities
  • Reconstruct money trails
  • Surface patterns
  • Retrieve legal authorities
  • Organize evidence
  • Identify questions for review

Will not do

  • Declare guilt
  • Predict conviction
  • Automatically establish “proceeds of crime”
  • Automatically establish a PMLA offence
  • Replace professional legal judgment
  • Generate unverified citations
Suspicion is an analytical signal, not a legal conclusion.

The vision

Start with the money trail.
Build toward legal intelligence.

  1. TodayTransaction Intelligence
  2. NextEvidence Intelligence
  3. ThenPMLA Legal Intelligence
  4. ThenJudgment & Precedent Intelligence
  5. Long termEnterprise Financial-Crime Intelligence Platform
Making complex financial evidence searchable, connected, explainable and legally useful.

The team

Built by people who work these cases

Cybex Legal grew out of everyday cyber-fraud casework: victims trying to recover savings, frozen accounts, and investigators racing to follow the money.

Adv. Pratik Panda

Founder

Advocate Pratik Panda is a legal professional practising in Siliguri and Kolkata, West Bengal, with a focus on cybercrime, digital financial fraud, criminal litigation, and bank account de-freezing matters. With CEH (Certified Ethical Hacker) and CHFI (Computer Hacking Forensic Investigator) credentials, he combines legal practice with a technical understanding of cybersecurity and digital forensics. He is committed to ethical advocacy, diligent case preparation, and helping clients explore appropriate legal remedies in complex cybercrime and financial fraud cases.

Adv. Deep Ghosh

Co-Founder

Advocate Deep Ghosh is a junior advocate practising in Siliguri and Kolkata, West Bengal. He is committed to developing his legal expertise through legal research, case preparation, and practical courtroom experience. With an interest in civil and criminal law, he strives to approach every legal matter with diligence, sincerity, and professionalism. He believes in upholding the principles of justice, integrity, and fairness while continuously strengthening his advocacy skills and contributing to the legal profession.

Get in touch

See your money trail in minutes

We’re speaking with law firms, investigators, financial institutions, incubators and early partners. Tell us about your work and we’ll set up a demo.

  • A walkthrough on an illustrative case
  • How source-linking and safeguards work
  • Early pilot and partnership options

Prefer email? info@sandslegaladvisors.com

Please don’t include confidential case details in this form.

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