Forensic Accounting Methods

Revenue recognition isn't about when you get paid Most founders mess this up. They see $12,000 hit their bank account and think they just made $12,000 in revenue. Wrong. You made $1,000 in revenue...if it's an annual contract. What is Revenue Recognition? Revenue is earned income from delivering goods or services. Recognition is when it's reported on your income statement. These happen at different times. You collect $12,000 upfront for an annual subscription. But you only earned $1,000 of that in month one. The other $11,000? That's deferred revenue sitting on your balance sheet. The Journal Entries: When the sale happens: Debit Cash $12,000 Credit Deferred Revenue $12,000 Each month as you deliver service: Debit Deferred Revenue $1,000 Credit Revenue $1,000 This moves money from your balance sheet to your P&L as you actually earn it. Daily vs Monthly Methods You can recognize revenue daily or monthly. Daily method: $12,000 ÷ 365 days = $33 per day Monthly method: $12,000 ÷ 12 months = $1,000 per month Both get you to $12,000 over the year. Daily gives more precision but monthly is simpler. The Base Formula Every deferred revenue balance follows this pattern: Beginning Balance + Additions - Subtractions = Ending Balance Additions = new cash collections Subtractions = revenue recognized Track this for every contract and you'll know exactly where you stand. The Manual Nightmare Most founders start tracking this in spreadsheets. Works fine for 10 contracts Gets messy at 50. Completely breaks at 100+. Picture this...you've got 50 active contracts. Each one has different start dates, different terms, different recognition schedules. You're tracking everything in Excel. Every month you need to: Update deferred revenue balances for each contract. Calculate how much revenue to recognize. Create journal entries for each one. Make sure everything ties to your GL. I've seen many people spending 3 full days every month just on revenue recognition. And you know what happened? They'd still find errors weeks later. Daily Method Makes it Worse. Think monthly is bad? Try daily recognition with multiple contracts. $12,000 annual contract = $32.88 per day $24,000 contract = $65.75 per day $6,000 contract = $16.44 per day Now multiply that by 50+ contracts...each starting on different dates. You're calculating different daily amounts for hundreds of line items. Automation Saves Your Sanity Maxio completely eliminates this pain. Set up your revenue recognition rules once. The system automatically applies them across every contract. Daily, monthly, whatever method you choose...it just works. 30 minutes to run reports and review everything. That's it. No more manual calculations, no more formula errors, no more audit trail headaches. Everything's automatically GAAP compliant and audit-ready. === How do you currently track your revenue recognition? #MaxioPartner

Understanding the methodology for customer risk profiling under the #AML framework. Is it sufficient to classify the customer as "high" or "low" risk merely based on their jurisdiction or person being a #PEP? The answer is NO! Customer Risk Assessment (#CRA) is an extensive process that assesses the ML/FT risk a customer poses. While evaluating this, a comprehensive view of all the parameters impacting the business relationship must be considered. This includes: ➡ Associated geographies (nationality, domicile, business operations) ➡ Outcome of screening (#Sanctions, PEP or presence of any #AdverseMedia) ➡ Nature of business activities ➡ Legal structure (complexity and transparency) ➡ Services or products involved ➡ Nature of the proposed transaction (frequency, value, consistency with customer's social/economic profile, etc.) ➡ Expected mode of payment ➡ Delivery channels (including involvement of third parties) ➡ Any other risk factors considering the nature and size of the business and the customer’s profile With a robust Customer Risk Assessment, strengthen your efforts around detecting and combatting financial crime. #AMLUAE #AntiMoneyLaundering #AntiFinancialCrime #CustomerRisk #RiskAssessment #CDD #SanctionsCompliance #EDD

💥 FinCrime Mythbusters 💥 Myth#13 〰️ Dormant Accounts ❌ Myth: Dormant accounts are low risk. If they are inactive, they don’t matter. ✔️ Reality: Accounts with little or no activity are prime targets for exploitation by both fraudsters and money launderers. 🔎 Why Dormant Accounts Matter in Practice In fraud prevention and AML, I have seen dormant accounts used in multiple ways: 📌 Account Takeover: Fraudsters hijack old, forgotten accounts knowing monitoring is weaker. 📌 Money Mules: Criminal networks open accounts, let them lie dormant, then bring them to life when needed to push illicit funds through. For example; student accounts handled by money mule herders. 📌 Exploiting Vulnerable Customers: Dormant accounts belonging to elderly, deceased, or less digitally engaged customers are prime targets for scams. 📌 Warehousing Criminal Funds: Illicit money can sit idle, waiting until investigators ‘move on’ before being withdrawn or layered. 📌 Terrorist Financing: Reactivating a dormant account to send funds around conflict zones/high-risk jurisdictions. 👉 UK Regulatory Context ⭕ The Dormant Bank and Building Society Accounts Act 2008 applies only after 15 years of inactivity. Until then, firms remain fully responsible. Balances can then be transferred into the UK’s Reclaim Fund, which redirects funds for social or community benefit. HOWEVER, proceeds can lie quiet and untouched for years, sometimes deliberately, account inactivity is also a tactic to avoid detection. ⭕ Under POCA 2002, funds remain criminal property regardless of inactivity. If you suspect proceeds of crime are in a dormant account, you must submit a SAR to the NCA. Even if no transactions occur, knowledge of suspicious origins alone can trigger a reporting duty. ⭕ MLRs 2017 require firms to apply a risk-based approach. Dormancy is a risk factor in itself. ⭕ If suspicious funds are identified, the NCA can apply for an AFO under the Criminal Finances Act 2017. 🗣️ The Right Approach ✅ Keep dormant accounts within ongoing monitoring ✅ Trigger EDD when dormant accounts ‘wake up’ ✅ Encourage Fraud & AML collaboration, for example; a dormant account is inactive for 4 years. Account is reactivated by login in from a new device, 2 small inbound credits followed by an outbound. Fraud triage finds SIM swap evidence while AML links outbound beneficiary to mule network. Joint team places temporary hold, gathers evidence, and then files a SAR ✅ Factor in customer vulnerability, not all flags indicate perpetrators, some indicate victims. Reactivation might be coercion or exploitation. ✅ Design Transaction monitoring system with separate dormant rules. The first sign must be a ‘reactivation’ followed by unusual transactions. ⚔️ Dormant accounts are like the Army of the Dead 🧟 silent, waiting, and underestimated. Just because they appear lifeless doesn’t mean they are not a threat. #FinCrimeMythbusters #AML #Fraud #SAR #FinancialCrimePrevention

Revenue recognition may be amongst the most dangerous blind spots in high-growth startups today. When I moved from the corporate world into investing in startups I was told, rightly, that in the early days, product-market fit matters more than financial statements. But I've come to believe that revenue recognition, in particular, is often left for too late. That's risky, especially once a startup crosses a certain revenue threshold. Investors have the most leverage to set the tone for robust revenue recognition, yet the spotlight tends to be on growth metrics like user acquisition, retention, engagement, and GMV. These drive valuations and funding milestones, but when actual revenue - and how it's recognised - takes a backseat, it creates fragility. The golden rule is simple: be conservative with revenue recognition once revenues start to scale. This isn't about introducing enterprise-level financial controls at the seed stage, but ensuring that stage-appropriate governance kicks in at the right time. Unfortunately, even in well-funded growth-stage startups, this often gets neglected. Loose revenue recognition practices may not be fraudulent, but they can be misleading. Common red flags include: 1. Upfront recognition of multi-year contracts: Booking the entire value upfront instead of spreading it over time. 2. Immediate recognition of non-refundable upfront fees: Treating setup fees as revenue right away instead of over the customer lifecycle. 3. Gross vs. Net revenue: Reporting full transaction value instead of just the commission in marketplaces. 4. Channel stuffing: Inflating revenue by pushing unsold inventory to distributors. 5. Premature recognition of trial revenues: Recognising revenue during free trials before payment commitment. Of course, enforcing strict revenue recognition too early can mis-allocate precious startup resources and distract from product and customer priorities. But once a company reaches meaningful scale, deeply evaluating and strengthening accounting practices is a must-do – else it becomes a risk. The problem? No one around the table has a strong incentive to make this a priority. While investors can absorb losses through portfolio diversification, founders face reputational damage, and the broader impacts are severe: job losses, customer fallout, funding freezes, and sector-wide credibility damage. Good revenue recognition practices won't win pitch decks. But once you're scaling, they build resilience, credibility, and trust. The inflection point typically arrives around Series B, when investor scrutiny intensifies, enterprise customers become more common, and your financial story directly impacts valuation and credibility. #startups #founders #venturecapital  

This is an ICFR case study at MSNA that amazed me this year Last year, we were working on an ICFR (Internal Controls over Financial Reporting) engagement, and we had identified a lot of internal control gaps, a lot of segregation of duties gaps, a lot of fraud risks, and we presented to the Board. The Board went through each risk, accepted many, and shot down a few risks as they said it was not practical to work on them, owing to the size of the organization. We tried to reiterate that keeping the risks open could lead to instances of fraud, as there are loopholes that can be made use of. We also gave process improvements as part of the report and ensured that no points were removed from our report to the Board. The Board agreed to the process improvements but could not implement them organization-wide as there was nobody to steer this forward. Fast forward to 2025, the fraud did manifest in the organization in the exact way in which we had informed the Board, using the same loophole which we had highlighted. Now, the Board was perplexed and asked us to do a forensic engagement as the board knew fraud had occurred, but not the quantum of the fraud and who was involved. When we did go deep, we tried multiple techniques to uncover the fraud and were finally able to establish the amount of fraud. This now became a stark reminder to the Board on how important internal controls are and how important it is to plug the loopholes from the root cause. This, for us is a case study which we tell all our new hires on how a fraud was predicted, manifested, and identified. With time, the Internal audit must show predictable scenarios and futuristic risks once the current risks are plugged. Boards must always ensure that the internal audit is risk-based and aids in business decisions rather than a mere checklist-based activity. I love how these engagements impact organizational processes and how good internal controls collectively save money for the organizations. Happy to suggest better controls if you are facing a roadblock :) Nitesh MN Ashwini Magod Madan Hemaraju #ca #charteredaccountant #cafirm #founder #financemanager #cfo #virtualcfo #icfr #internalaudit #internalcontrols

Most people don’t really understand accrual-basis accounting. And honestly? That’s understandable. It’s one of those “grown-up” accounting terms that gets tossed around in meetings, in reports and on CPA exams but rarely explained in a way that makes it click. So here’s how I explain it: Imagine you run a lemonade stand. You sell a glass of lemonade to your neighbor today, and she says, “I’ll pay you next week.” 👉 Accrual-basis accounting says: Count that sale today. You earned it. Now, you use a box of lemons today that you previously purchased "on account" which means you won’t pay the store until sometime in the future, maybe next month. 👉 Accrual-basis accounting says: Record the cost today. You used the lemons. In short: — Record revenue when it’s earned (not when cash arrives) — Record expenses when used (not when cash leaves) That’s how you get a true picture of how your business is doing — even if no money has changed hands yet. 💼 And here’s a real example from my own experience: When I was a VP at Kaplan, I had to understand accrual accounting to make effective strategic decisions. Let’s say I needed to increase quarterly revenue by $10 million. If the product I sold was recognized as revenue over a period of time, that meant I couldn't recognize the revenue until the performance obligation was met. So when the sale was made, I had to record deferred revenue which shows up as a liability until it's earned instead of revenue. So to hit that $10M revenue quarterly goal? I had to sell much more than $10M. If you don’t understand how and when revenue is recognized, you can’t make sound financial decisions. It’s that simple. If you want to go deeper, take my popular LinkedIn Learning course: “Accounting Foundations: The Accounting Cycle and Accrual Basis Accounting” It’s NASBA-approved for CPE and designed to make accounting make sense — especially for non-accountants and decision-makers. And I have decided to make it free for you for a limited time if you use this link: https://lnkd.in/gEEYzr4R

What’s Revenue Recognition and why can it make or break your funding round? Imagine you run a toy shop. A customer pays you ₹1,000 today for a toy that you’ll deliver next month. Do you count that ₹1,000 as today’s revenue? No. Because you haven’t delivered the toy yet. That’s revenue recognition. You only record sales when you’ve actually delivered what you promised. Here’s where it gets tricky in real businesses: SaaS startups: Collect a year’s subscription upfront. If they count all of it today, their P&L looks inflated until an investor digs deeper. Exporters: Ship goods in March, but payment clears in April. Which financial year does it belong to? D2C brands: Marketplace shows “sales booked” but half are returns. If you book it all as revenue, your numbers are not real. At FAB MAVEN, we’ve seen this repeat often: Startups showing “hockey-stick growth” but without factoring return rates. SaaS firms losing credibility when MRR ≠ reported revenue. Exporters paying tax on money not yet received. Revenue recognition isn’t just an accounting rule but it creates a legit difference between appearing fundable and actually being fundable.  Have you ever caught a revenue number in your business that looked too good to be true?

𝐊𝐢𝐧𝐠 𝐕 𝐡𝐚𝐬 𝐣𝐮𝐬𝐭 𝐜𝐡𝐚𝐧𝐠𝐞𝐝 𝐭𝐡𝐞 𝐠𝐚𝐦𝐞 𝐟𝐨𝐫 𝐄𝐭𝐡𝐢𝐜𝐬 𝐚𝐧𝐝 𝐅𝐫𝐚𝐮𝐝 𝐑𝐢𝐬𝐤 𝐆𝐨𝐯𝐞𝐫𝐧𝐚𝐧𝐜𝐞. The conversation has shifted dramatically: ❌ 𝐍𝐨 𝐥𝐨𝐧𝐠𝐞𝐫: "Do you have ethics policies?" ✅ 𝐍𝐨𝐰: "Can you evidence their impact on ethical culture?" King V doesn't take your word for it. It demands tangible proof. 𝐒𝐢𝐱 𝐜𝐫𝐢𝐭𝐢𝐜𝐚𝐥 𝐜𝐡𝐚𝐧𝐠𝐞𝐬 𝐄𝐭𝐡𝐢𝐜𝐬 𝐚𝐧𝐝 𝐅𝐫𝐚𝐮𝐝 𝐑𝐢𝐬𝐤 𝐏𝐫𝐚𝐜𝐭𝐢𝐭𝐢𝐨𝐧𝐞𝐫𝐬 𝐨𝐮𝐠𝐡𝐭 𝐭𝐨 𝐤𝐧𝐨𝐰: 1. 𝐄𝐭𝐡𝐢𝐜𝐬 𝐢𝐬 𝐚 𝐛𝐨𝐚𝐫𝐝‑𝐥𝐞𝐯𝐞𝐥 𝐊𝐏𝐈: Culture indicators, leadership behaviour, and whistleblowing responsiveness are now measurable governance outcomes that require evidence-based reporting. 2. 𝐃𝐢𝐠𝐢𝐭𝐚𝐥 𝐞𝐭𝐡𝐢𝐜𝐬 𝐢𝐬 𝐚 𝐠𝐨𝐯𝐞𝐫𝐧𝐚𝐧𝐜𝐞 𝐫𝐞𝐪𝐮𝐢𝐫𝐞𝐦𝐞𝐧𝐭: AI-enabled fraud, algorithmic bias, deepfakes, and data manipulation are recognised as core governance risks, reflecting how fraud has evolved into digital ecosystems. 3. 𝐅𝐫𝐚𝐮𝐝 𝐫𝐢𝐬𝐤 𝐢𝐬 𝐞𝐦𝐛𝐞𝐝𝐝𝐞𝐝 𝐬𝐭𝐫𝐚𝐭𝐞𝐠𝐢𝐜𝐚𝐥𝐥𝐲: King V integrates fraud risk across the entire value chain, from supply chain vulnerabilities to ESG reporting integrity, making it a strategic imperative rather than an operational function. 4. 𝐋𝐞𝐚𝐝𝐞𝐫𝐬𝐡𝐢𝐩 𝐚𝐜𝐜𝐨𝐮𝐧𝐭𝐚𝐛𝐢𝐥𝐢𝐭𝐲 𝐢𝐬 𝐞𝐧𝐟𝐨𝐫𝐜𝐞𝐚𝐛𝐥𝐞: Boards must demonstrate proactive consequence management, transparent conflict oversight, and ethical decision-making frameworks as governance requirements. 5. 𝐂𝐨𝐦𝐛𝐢𝐧𝐞𝐝 𝐚𝐬𝐬𝐮𝐫𝐚𝐧𝐜𝐞 𝐢𝐧𝐜𝐥𝐮𝐝𝐞𝐬 𝐞𝐭𝐡𝐢𝐜𝐬 𝐚𝐧𝐝 𝐟𝐫𝐚𝐮𝐝: The first, second, and third lines of defence must align on ethics, fraud risk, compliance, and technology controls. Fragmented assurance is a governance failure. 6. 𝐄𝐒𝐆 𝐢𝐧𝐭𝐞𝐠𝐫𝐢𝐭𝐲 𝐜𝐨𝐧𝐧𝐞𝐜𝐭𝐬 𝐭𝐨 𝐟𝐫𝐚𝐮𝐝 𝐠𝐨𝐯𝐞𝐫𝐧𝐚𝐧𝐜𝐞: Carbon credit fraud, greenwashing, and climate-related misstatements are explicitly recognised as ethics and fraud risks requiring governance oversight. 𝐓𝐡𝐞 𝐬𝐡𝐢𝐟𝐭: Ethics, technology, and fraud governance have converged. Organisations must demonstrate credibility through evidence, not just compliance documentation.

Free Resource Friday! If 2024 has taught us anything, it’s that fraud remains one of the most critical compliance challenges we face today. From APP fraud to synthetic identity scams, money muling, and cross-border fraud, financial criminals are evolving faster than ever. For compliance professionals, fraud risk assessment is no longer optional—it’s a must-have for proactive risk mitigation, regulatory alignment, and reputational protection. 🔍 ACAMS has released a FREE best practice guide on Fraud Risk Assessment, offering: ✔️ A step-by-step methodology for conducting robust fraud risk assessments ✔️ Insights into emerging fraud threats and trends ✔️ A risk prioritization matrix to help organizations focus on high-impact risks ✔️ Strategies to break down silos and create a multi-disciplinary fraud risk approach ✔️ Real-world examples, frameworks, and a fraud risk register template 💡 Key takeaway? Fraud is not just an AML issue—it’s an enterprise-wide risk. Organizations that embed fraud risk assessments into their compliance framework will be better equipped to handle regulatory changes and reduce financial crime exposure. #FraudPrevention #Compliance #RiskAssessment #ACAMS #FinancialCrime

⚡ How Banks Detect Card Fraud in Under 100 ms Every time you tap your card, an incredible amount of analysis happens before your transaction is approved—usually in less than 100 milliseconds. Let's see what happens behind the scenes. 💳 Step 1: Transaction Initiated You tap your card at a POS terminal. An ISO 8583 authorization request is created and sent through: POS Terminal ↓ Acquirer Bank ↓ Visa / Mastercard ↓ Issuer Bank The issuer now has only a few milliseconds to decide whether the transaction is genuine. 🧠 Step 2: Fraud Engine Takes Over Before checking your account balance, the issuer's Fraud Detection Engine evaluates the transaction using hundreds of rules and AI models. It analyzes signals such as: 📍 Location Check Is the transaction happening in a location consistent with your recent activity? Example: A purchase in London just minutes after one in Delhi is suspicious. 💰 Transaction Amount Is the amount unusual for this cardholder? ⚡ Velocity Check Have there been multiple transactions within a very short time? Example: 5 purchases in 2 minutes. 🏪 Merchant Category (MCC) Does the merchant type match your normal spending behavior? 📱 Device & Channel Is this a trusted device or payment channel? 📊 Behavioral Analysis Does this transaction fit your historical spending pattern? 🚫 Blacklist & Watchlists Is the card, merchant, IP address, or device already flagged? 🤖 Step 3: AI Generates a Risk Score All these checks are combined to calculate a risk score. Risk Score < 30 ↓ Approve ✅ Risk Score 30–70 ↓ Step-up Authentication (OTP / 3DS) Risk Score > 70 ↓ Decline ❌ This decision is made in just a few milliseconds. ⏱️ Example Timeline 0 ms → Card tapped 20 ms → Authorization reaches issuer 45 ms → Fraud engine evaluates risk 75 ms → Decision made 95 ms → Response reaches POS The customer only notices a brief "Processing..." message, while the bank has already analyzed hundreds of data points. 🛡️ Why It Matters Modern fraud detection isn't based on a single rule. Banks use a combination of: Rule-based engines Machine Learning models Real-time behavioral analytics Device fingerprinting Historical transaction patterns to stop fraudulent transactions before money leaves the account. 💡 Key Takeaway Banks don't just check your balance—they evaluate every transaction against hundreds of risk signals in under 100 milliseconds before deciding whether to approve or decline it. Every time you tap your card, an AI-powered fraud engine races against the clock—analyzing hundreds of signals and making a decision in under 100 milliseconds. That's the invisible technology protecting billions of transactions every day.