Links

In our last group assignment,we started by determining our red flags and eventually ended up modifying the red flags multiple times; each iteration bringing us closer to the fraudster. As research, I decided to study red flags in more detail, as they have proved vital in process mining for fraud detection.

Main types of red flags are:

Structural:  Red flags that catch fraud due to the way the company is set up and the policies/procedures that are in place. An example is the type of fraud that happens when an employee realises what size of transaction creates added scrutiny. This kind of fraud can be discouraged if Management leads by example, with ethical behavior exhibited at all times

Operational: Red flags that highlight how the company business is managed each day. Are they minimizing the chance for employee errors and having checks in place? Key concerns for management should be segregation of duties so that no employee has too much control over one area.

Accounting: Red flags that refer to the level of internal controls that are in place. The company cannot have secure accounting free from error without such controls built into their FIS. Some of the basic red flags that might be noted in a company’s accounting records include frequency of transactions. Every company has its own operating patterns, and the transactions should be booked accordingly.

Financial performance: Red flags include aggressive goals and performance measures.Companies whose financial performance suggests the possibility of fraud have signs like outstanding results when the rest of the industry has suffered a downturn.

I feel that learning to classify flags properly might help students look for each type of flag in different areas in the project, and systematically track fraudsters.

Reference:

Essentials of Corporate Fraud by Tracie L. Coenen

Should a large corporation model its information system or structure the same way a Startup does?

Well according to this article, the author claims you shouldn’t and I agree. The article stresses that current corporations are attempting to innovate or model their systems by using this analogy of “throwing mud at a wall, slowly and expensively.” Meaning using different methods without much planning at a slow and expensive rate.

How does this relate to Financial Information Systems? Well for one, FIS systems should not be treated like a Startup app such as Snapchat. FIS systems especially ones used in the banking industry must be well planned and tested. The Startup methodology of quickly releasing a product to market and releasing updates to fix bugs and glitches is a poor method for banks that house sensitive and confidential customer information.

It’s understandable, thats companies such as banks would want to quickly release new services or updates to their system like a Startup but when it comes to Financial Information Systems and a business organization like a bank in which hackers are constantly looking for loop holes in their security. The model of “build, measure, learn” cannot be used for the FIS. Rather Financial Information Systems should learn, measure, then build.

How Do Trends in Big Data Affect Information Systems and How Can Companies Improve Their Services?

While Information Systems provide better organization, communication, and efficiency of data across different departments. What happens when high volumes of data are brought to the system and we mean “Big Data”?

As stated by the author, Rich Sokolosky the key essence of information systems or SaaS provider is to deliver the right data at the right time to whoever needs it. While the traditional method is the data-warehouse model, the trend is now moving towards cloud mobility. While a company may have their own in-house Information System, with the need for accessibility and speed many companies are looking to move their system to the cloud.

This article was written directly for SaaS companies that are looking to provide services such as data analytics from Information Systems and delivering it to their customers. The author claims, that while it is do-able there are a lot of complex factors that make this reality difficult.

The author’s main suggestion for these big data companies is simple: “Start small, provide low-cost, and deliver answers quickly.” In fact the author is challenging smaller companies to compete against big players. The author’s key message is that “there is always room for improvement”. While I agree with the author, as information systems are gathering “big data”, the question now becomes how can SaaS companies gather analytics and deliver to their clients quality data in a timely manner?

Link to the Article Here

My views on Wells Fargo case:

By analyzing the case we can look at Two problems technology and people. In technology part the users didn’t receive any form of notifications like email, SMS etc. when the false accounts were created or credit cards were issued. So the system might not be based on the customer ID which gives the customer a holistic view of all the accounts. It suggests there is a loophole in the system which has been exploited. Imagine a case where these accounts could be used for illegal activities. It’s not just about creating accounts but the way they could use people information without their knowledge. Coming to the second one, people this case is little different from management was not directly involved in the case but when we look at the big picture the practice was across the bank which makes it look like a company practice rather than malpractice. The management greed for bonuses made thousands of employees lose their jobs. But how can one forget the simple fact that in transaction system the entry once made would be there forever and any inquiry in future would reveal the culprits.

References:http://www.forbes.com/pictures/eedh45gefdi/185-million-in-fines/#3a70ef5139d8

http://money.cnn.com/2016/09/21/investing/wells-fargo-fired-workers-retaliation-fake-accounts/http://www.chicagotribune.com/news/opinion/commentary/ct-wells-fargo-scandal-arrogrant-fraud-perspec-0922-20160921-story.html

http://www.wsj.com/articles/wells-fargo-where-was-the-auditor-1478007838