IASIU CIFA study plan

Define the fraud analyst role in writing by contrasting it with auditor, investigator, and compliance roles across five distinguishing criteria

Map the fraud triangle and fraud diamond to two real public case summaries, listing which analytical skill surfaced each red flag

Build a personal competency gap table scoring yourself 1-5 on each of the twelve exam domains with a one-line justification per score

Practice two analytical reasoning drills (trend break identification, outlier justification) and record the reasoning steps you used

Apply a structured problem-solving cycle (frame, hypothesize, test, conclude) to three short fraud scenarios and document each step

Distinguish correlation from causation in four analytical claims by naming the confounders and the additional test each would require

Select appropriate detection techniques (exception reporting, Benford analysis, duplicate testing, ratio analysis, link analysis) for five stated fraud risks and justify each match

Identify cognitive biases (confirmation, anchoring, availability) in three sample analyst memos and rewrite the flawed reasoning

Classify ten sample data elements as structured, semi-structured, or unstructured and state the analytical limits of each type

Define data quality dimensions (completeness, accuracy, consistency, timeliness, validity) and score a sample extract against each with counts

Sketch a normalized relational schema for a payments dataset showing primary keys, foreign keys, and one-to-many relationships

Write five join questions against that schema in plain English and predict the row count behavior of each join

Execute the full analysis lifecycle (scope, acquire, clean, transform, analyze, validate, report) on a provided transaction file and log every step taken

Clean a deliberately messy dataset by resolving duplicates, standardizing dates and vendor names, and documenting each transformation rule

Build Excel formulas using VLOOKUP or XLOOKUP, SUMIFS, COUNTIFS, and IFERROR to produce five summary metrics from the cleaned data

Create a pivot table that compares spend by vendor and month and flag the top three anomalies with written justification

Apply conditional formatting, data validation, and duplicate-detection formulas to surface exceptions in a transaction list

Construct a Benford's Law test in Excel using LEFT and COUNTIF and interpret the first-digit deviation for a real extract

Choose the correct chart type (bar, line, scatter, histogram, box plot) for six analytical questions and justify each choice

Design a one-screen dashboard that answers a stated fraud question with no more than four visuals and a clear headline

Write SELECT statements using WHERE, ORDER BY, GROUP BY, and HAVING to answer ten fraud questions against a sample database

Combine tables with INNER and LEFT JOIN and explain when each join would hide or reveal a fraudulent record

Use aggregate functions and subqueries to isolate vendors whose invoice totals exceed a threshold or deviate from their peer group

Identify three classic SQL-based fraud tests (duplicate invoices, split payments below approval limits, off-hours entries) and write each query

Write a Python or R script that loads a CSV, cleans it, computes grouped summary statistics, and exports results to a new file

Use a loop and a function to repeat a duplicate-detection test across multiple columns and explain what the function returns

Draft a five-minute verbal briefing and a one-page written summary of a finding, each leading with the conclusion and quantifying the exposure

Anticipate and answer three likely challenge questions from legal, audit, and management audiences in a mock review

Synthesize all twelve domains into a single end-to-end case walkthrough from data request through reported conclusion

Complete a timed 60-question practice set across all domains and review every missed item to its underlying concept

Refine the findings brief from week 7 using feedback and add a limitations and data-quality section

State your professional ethical obligations around data handling, confidentiality, and objectivity in a short written position