Forensic accounting, fraud investigation and fraud analytics

Target six fraud types with fraud auditing, including embezzlement, extortion, suspicious transactions, and conflicts of interest, and categorize them by industry as bank, corporate, insurance, healthcare, cyber, and consumer fraud.

Explore bank fraud across deposits, underground economy, and transactions, including identity theft, and credit or debit card fraud from skimming, card interception, or merchant compromise, plus check fraud tactics.

Explore corporate fraud and how employees may commit occupational fraud for personal gain. Learn three main categories: corruption, misappropriation, and fraudulent financial statements, plus their subcategories.

Explore the fraud tree within the occupational fraud and abuse classification system, detailing corruption, asset misappropriation, and financial statement fraud with examples like bribery and theft of cash on hand.

Explore the classification of occupational fraud, including misappropriation, corruption, and financial statement fraud, with examples like over invoicing, related party transactions, and window dressing.

Explore how payroll fraud emerges through ghost employees, timesheet manipulation, misclassification, and pay scale abuse, and learn prevention through segregation of duties, checks and balances, and access controls.

Examine procurement fraud across the procure-to-pay lifecycle, including phantom vendors, bid rigging, change order abuse, unallowable charges, and procuring false invoices to show how collusion causes billions in losses.

Examine platform fraud across social, financial, and enterprise platforms, covering identity theft, payment fraud, ransomware, and data breaches, with red flags and controls to reduce financial losses.

Examine how payment fraud threatens the e-commerce sector with credit card fraud, identity theft, and account takeover. Discover how merchants use 3D Secure and multi-factor authentication to prevent attacks.

Examine phoenixing, where a liquidated company restarts under the same name to dodge debts, and asset stripping, where undervalued assets are sold for cash, often by private equity.

Analyze money laundering from placement through layering and integration, including online banking and cryptocurrencies, and examine anti money laundering measures, tax evasion, and corporate espionage like hacking and insider transfers.

Asset misappropriation is the most common form of occupational fraud, with methods like skimmed cash, refunds, fraudulent billing, inventory misstatements, and inflated expenditure reports aided by deception.

Define fraud and corruption, including bribery, kickbacks, and abuse of public power, and examine contract-related schemes such as bid rigging, price fixing, duplicate payments, phantom contractors, and misappropriation.

Identify startup fraud risks by examining false financial statements, overstated technology, and misrepresented management or investors, and flag red flags like unavailable financials, lawsuits, unrealistic promises, and pressure tactics.

Explore fraud types across insurance, cyber, securities, and consumer sectors, including premium diversion, false claims, and identity theft. See how forensics and risk management assess claim veracity and detect fraud.

Explore whistleblowing in corporate fraud, including internal and external disclosure, types like open, personal, and impersonal whistleblowing, and the retaliation risks and legal protections that accompany claims.

Explore the basics of forensic accounting, fraud investigation, and fraud analytics, including fraud detection, computer forensics, internal controls, and expert testimony in legal proceedings.

Explore how forensic accountants render services such as criminal investigation, professional negligence assessment, arbitration and mediation services, fraud investigation, risk assessment and risk mitigation, insurance claim settlement, and dispute settlement.

Identify red flags as warning indicators of potential fraud, including aggressive financial goals, unusual cash flow, irregular financial ratios, and suspicious accounting patterns signaling weak internal controls.

Examine operational, behavioral, structural, and personal red flags signaling fraud risk. Learn indicators such as living beyond their means, financial difficulties, and policies, procedures, and monitoring shaping opportunities for fraud.

Identify yellow flags as potentially legitimate but suspicious activity needing verification, such as unusual debit card amounts or logins from unusual locations, and recognize green flags as nonconclusive indicators.

Use green flags, red flags, and yellow flags to identify financial fraud risk; green flags indicate honesty, red flags indicate risk, and yellow flags require further investigation.

Conduct effective interviews to gather facts and identify deception, then report findings clearly to support credibility and actionable outcomes in internal investigations.

Collect data from diverse sources—phone records, CCTV, ERP logs, and access details—to guide interviews. Align the interview plan with the investigative framework, set timing and locations to gather evidence effectively.

Learn to prepare and conduct fraud interviews, coordinate with HR and IT, set goals, assign interview roles, manage recordings and chain of custody, and handle rapport with sensitive questions.

This lecture outlines informational, open, closed, leading, and other question types used in fraud investigations. It emphasizes nonconfrontational, unbiased questioning that gathers information, invites thorough responses, and allows for verification.

Prepare for interviews by researching, gathering primary documents, and rehearsing; use a calm, neutral demeanor and move from broad to focused questions with data mapping.

Identify the seven investigative tools for fraud examination, including public document reviews, background investigations, public databases, corporate records, internet research, interviews of knowledgeable persons, and witness evidence.

Examine investigative tools for forensic accounting, including physical examination, fingerprint analysis, document dating, computer forensics with hard disk imaging and email analysis, surveillance, financial transaction analysis, and data mining.

Apply trend analysis to review historical norms and spot anomalies or outliers in financial data. Use ratio analysis and key financial ratios to assess a company's health and estimate overheads.

Use the juxtaposition test to verify document authenticity by comparing format and content side by side, spotting red flags like same writing across two different formats.

Discover the test of absurdity in forensic auditing to identify fraudulent activity by flagging absurd entries, and compare it with ratio analysis, trend analysis, and data mining techniques.

Apply the test of impossibility in forensic audits to detect fraud by identifying profits that cannot be reconciled with asset and liability totals.

Apply ratio analysis of quarterly maintenance expenditure to detect anomalies in a forensic accounting case study, illustrating a kickback fraud where a store manager overpays a brother-in-law's heating company.

Identify how the RSF (relative size factor) reveals isolated outliers in normal transaction patterns and aids auditing for fraud, miscalculations, and errors.

Benford's law shows the first leftmost digits favor lower values, enabling data analysis, validation, and fraud detection in forensic accounting and audits.

Apply Benford's law to forensic auditing by analyzing the first significant digit and comparing actual versus predicted frequencies to spot fraud, assess data quality, and guide risk assessment.

Explore Benford's law through real-world fraud examples in accounts payable and travel expenses, revealing digit-level anomalies and how to apply the Benford formula in Excel to detect write-offs.

the Beneish model uses eight financial ratios, including DSRI, GMI, AQI, SGI, DEPI, SGAI, LVGI, and CRAI, to compute the m-score; original cutoff -1.78, revised -2.22.

Compare Benford's law and the Beneish model for fraud detection in financial data, highlighting digit frequency and eight financial ratios to flag earnings manipulation.

Learn the Luhn (modulus ten) algorithm to validate credit card, IMEI, and social insurance numbers by checksum calculations. Auditors use it to detect fraud by verifying identification numbers.

Apply fuzzy matching to identify duplicate or near-duplicate records in large datasets by comparing field values and adjusting similarity thresholds to reveal potential fraud.

Explore data mining techniques to uncover patterns in large data sets for forensic accounting, covering discovery, predictive modelling, deviation analysis, and key methods like classification, clustering, and association rule learning.

Explore Benford's Law for leading digits, compare the Finnish and Danish models for earnings manipulation, and examine fuzzy matching and matching techniques in forensic audit.

Trace every transaction to a digital footprint and present digital evidence in a court of law, using cross drive analysis, live analysis, recovered deleted files, steganography, and forensic software workflows.

Encase forensic investigation software enables efficient case management with data collection, triage, decryption, automated processing, indexing, and flexible reporting, while MD5 hashing verifies file integrity with 128 bit hash values.

Explore UFED and Oxygen forensic detective tools for extracting and analyzing mobile data, including contacts, messages, and media, from devices, SIM cards, and cloud sources.

Explore steganography, the art of concealing messages in cover media, and its relation to encryption, while applying forensic detection methods—statistical, image, and audio analyses—and noting legal implications.

Explore computer assisted auditing techniques (CAAT) and tools to process client information system data, test transactions and balances, and identify inconsistencies using generalized audit software and data analysis tools.

Explore generalized audit software (gas) for data extraction, manipulation, summarisation, and analysis in forensic accounting, with a user-friendly interface and features like statistics, duplicates and gaps, and Benfold analysis.

Explore common software tools for forensic accounting, such as spreadsheets like Excel and Lotus, and apply computer aided audit steps—checking missing or duplicate data, classification, and stratification.

Identify data gaps and anomalies in financial records—missing check numbers and data fields, duplicates, round or odd numbers, and isolated extreme values—then apply classification and stratification to target fraud risks.

Explore acl, an audit command language and data extraction and analysis software, also known as Galvanize, used by auditors for fraud prevention, risk management, and computer assisted audit technology.

Differentiate persistent data that remains when off (hard drives, USB) from volatile data lost when off (history, temp files). Apply forensic workflow: shut down, document hardware, transport, and back up.

Learn network forensics, a digital forensics subfield that analyzes traffic and protocols to investigate cyber crimes and security incidents. Use sniffers, firewalls, IDS, and IPS to identify sources.

Explore USB forensics and email forensics, detailing data recovery from USB devices and preserving email data, through acquisition, examination, analysis, and reporting across criminal investigations, civil litigation, and corporate espionage.

Master cloud forensics for collecting, analyzing, and preserving evidence in cloud environments across providers and actors, and apply hard disk forensics to recover data amid encryption and wiping challenges.

Create an exact copy of a storage device through disk imaging to preserve data, recover deleted files, and reveal evidence of fraud or malicious activity, aiding forensic audits.

Apply hashing to generate a unique hash value for files or datasets, verifying authenticity and detecting changes during migration, backups, or audits, with MD5 as an example.