The End of Risk: Minimising Fraud Through Big Data
As Big Data continues to grow exponentially, the spotlight is turning to one very important area of focus: fraud. Fraud comes in many forms—particularly in the financial sector. According to the Australian Institute of Criminology, fraud costs over $6 billion each year in Australia alone. To put this into perspective, around one out of every eight dollars spent fighting crime is spent on fraud.
As we move towards an increasingly globalised economy, there’s no doubt that bringing the fight to the fraudsters is vital. Combating fraud is a necessary action in modern business and this is precisely where Big Data can help your company do more.
To put it simply, Big Data is all the information your business may have collected or stored. This includes:
- Customer data
- Invoicing/purchase records
- Financial records
- Inventory
However, the potential of this information goes far beyond what meets the eye. In fact, understanding relationships between data points can yield a variety of useful insights related to your operations. Using the right data intelligence software can also help you minimise fraud in your business and, consequently, help in the prevention of its spread and deter the shady minds behind it.
How Big Data Helps to Reduce Fraud
The key to utilising Big Data to combat fraud boils down to deeper data interpretation. With the right type of analysis, you’ll be amazed at the insights you’ll discover.
Here are a few examples:
- Improved customer profile: Your data can help you better understand your customers in critical areas, such as behaviour and product and service usage. For instance, you can get an idea of how frequently customers access their accounts on either mobile or desktop devices, helping you to quickly detect unusual suspicious behaviour and action. This could include an overly high number of account access requests.
- Trend comparisons over time: Data insights will highlight the customer path which leads to discrepancies across your business operations that you may not have otherwise seen. Perhaps your data analysis shows higher inventory losses while certain employees are working, for you to use this information to investigate and determine whether fraud or theft is occurring.
- Cross-referencing for unusual activity: Big Data analytics can be quickly cross-referenced in search of anything suspicious. By quickly assessing these trends, you’ll gain a much clearer picture of what your business transactions look like when all is well—and alternatively when something may be amiss.
Case Study on Fighting Fraud with Big Data: Alibaba
Chinese e-commerce firm Alibaba has emerged as a highly recognised source for financial transactions throughout China. Because fraud is an extremely common problem within Chinese businesses, the firm has used Big Data to help in their preventative measures. Alibaba utilised a 5-step authentication process to legitimise transactions:
- Check and verify account
- Verify device used for transaction
- Review account activity
- Process information with risk strategy implementation
- Manually review flagged transactions
The fourth step is where the real “magic” happens. Each step compares a specific account with their normal account-based information (e.g. type of transaction, location, and device where the account was accessed, and recipients of transactions). But all of these data points come together within the overall risk potential when unusual account activity is detected. From there, the specific account may be manually reviewed to verify its legitimacy and ensure no fraudulent activity occurs.
This type of analysis is possible thanks to the power of Big Data. Without it, Alibaba would face a much higher level of fraud without any effective method to combat it before it happens.
Big Data to Shift Fraud Detection from Reactive to Proactive
In the past, measures to combat fraud were more reactive to what happens. Companies would eventually detect fraud and investigate. In most cases, the damage was already done.
With data intelligence working for you, this dynamic moves towards a more proactive model. You can use data analytics to more quickly identify a variety of factors related to potential fraud. From unusual financial activity to internal or security risks, Big Data holds the answers to your security-related questions. You just need the right data platform and interpretation capability to unlock those hidden insights.
How Your Business Can Stay Safe with Big Data
The application and benefits of using Big Data goes far beyond improved operational efficiency or sales performance. The right strategy can also help you better understand customer behaviour, helping you to identify signs of fraud or misuse before major damage occurs.
What’s more, this is achievable without having vast fraud detection resources. Gaining an effective model of protection based on Big Data is possible even for smaller businesses.
Companies that specialize in data interpretation such as Latize—with their semantic processing software Ulysses—provide businesses with a direct access point to data insights that can be used to detect and combat fraud. The highly intuitive Ulysses presentation layer helps your company gain better data insights, understand how the data relates to performance and operations, and identify and overcome instances of fraud. Over time, you will build up a robust defence against fraud for the future.
Processes and software such as Ulysses help you make these vital data connections and relationships within your business through an intuitive, semantic processing methodology. This analysis will yield valuable information relevant to your specific company operations. It is this type of innovation and real change that helps minimise fraud risk, positioning you for success in an ultra-competitive market filled with security threats, and FinTech dynamics.
