ACF5120 Week 3


Nah di minggu ketiga ini aku akan belajar 2 ban tentang fraud detection yaitu

Chapter 5: Recognizing The Symptoms of Fraud

Chapter 6: Data-Driven Fraud Detection

Recognizing The Symptoms of Fraud

Nah, di bab sebelumnya udah dikasih tau bahwa fraud bisa dikenali dengan beberapa tanda yang istilahnya Red Flags. Nah jika fraud symptoms ini sudah dikenali maka banyak fraud yang bisa di detect secara dini demi mencegah fraud tersebut menjadi lebih luas.

Symptoms fraud bisa dibagi menjadi enam kriteria:

  1. Accounting Anomalies: result from unusual processess or procedures in the accounting system, such as: problems with source documents, faulty journal entries and inaccuracies in ledgers. Some common journal entry fraud symptoms are: Journal entry without documentary support, unexplained adjustments to receivables/payables/revenues/or expense, Journal entry that do not balance, Journal entries made by individual who would normally make such entries, and journal entry made near the end of an accounting period.
  2. Internal Control Weaknesses, such as lack of segregation of duties, lack of physical safeguard, lack of independent checks, lack of proper authorization and so on
  3. Analitycal Anomalies: are relationships in financial or nonfinancial data that do not make sense, such as an unreasonable change in a volume, mix or price, unexplained inventory shortages or adjustments.
  4. Extravagant lifestyle, such as: have a very expensive cars, houses or wear expensive clothes and jewelry
  5. Unusual Behaviour: Jadi buat pelaku yang baru pertama kali melakukan fraud, mereka akan merasa bersalah secara emosional yang mendukung ke arah perubahan tingkah laku. Guilt–>Fear–>Stress–>Behaviour Changes such as: insomnia, taking drugs, increased drinking, inability to look people in the eyes, inability to relax, increased smoking.All of this can be done by observing the elements of fraud (theft act, concealment and conversion), and company employees are in the best position to detect fraud.
  6. Tips and Complaints, maksudnya adanya tips dan komplain dari pihak-pihak sekitar seperti customers, vendors, pegawai. Apapun tips dan kompain tersebut harus diperlakukan dan dipertimbangkan sebagai fraud symptoms, karena kebanyakan fraud tidak dapat dideteksi secara langsung oleh auditors. Individuals should always be considered innocent until proved gulty and should not be unjustly suspected or indicated.

Data-Driven Fraud Detection

Data-driven methods are a synthesis of many different knowledge areas, including fraud, audit, investigation, database theory, and analysis techniques.

Accounting Anomalies and Fraud

Accounting anomalies are primarily caused by control weaknesses and are not intentional mistakes. They are simply problems in the system caused by failures in systems, procedures or policies. For example: a typical anomaly might be double payment of invoices because of printer errors. On the other hand, fraud is the intentional circumvention of controls by intelligent human beings. Perpetrators cover their tracks by creating false documents or changing records in database systems. Evidence of fraud nay be found in very few transactions-sometimes only one or two.

Full population analysis is often the preferred method in a fraud investigation dan beruntungnya kebanyakan data sekarang dalam bentuk elektronik, sehingga metode full populasi bukanlah hal yang sulit untuk dilakukan.

The Data Analysis Process

There are 6 steps in the data-driven approach to fraud detection:

Analytical Steps (3 steps):

  1. Understand the business: setiap organisasi memiliki lingkungan usaha yang berbeda, sehingga the same fraud detection procedures can not be applied generally to all business or even to different units of the same organization.
  2. Identify Possible Frauds that could exist: requires an understanding of the nature of different frauds, how they occur, and what symptoms they exhibit. The fraud identification process begins by conceptually dividing the business unit into its individual functions or cycles.
  3. Catalog possible fraud symptoms (accounting anomalies, weaknessess of internal control, alaytical anomalies, extravagant lifestyle, unusual behaviour dan tips and complaints)

Technology Steps (2 steps):

  1. Use technology to gather data about symptoms. Once symptoms are defined and catalogued/correlated wit specific frauds, supporting data are extracted from corporate databases, online websites and other sources.
  2. Analyze result. Once anomalies are refined and determined by the examiners to be likely indications of fraud, they are analyzed using either traditional or technology-based methods.several fraud analysis techniques, such as: digital analysis, stratification and summarization, trending and text matching.

Investigative Steps (1 step):

  1. Investigate symptoms. Investigators should continue to use computer analyses to provide support and detail.