6 Ways Big Data helps Companies Mitigate Risks

What can the data-loving marketer of today learn from brands like American Express, BDO and Xerox when it comes to data and risk prevention? Staff writer Vandita Grover tells us. 

Gathering extremely large sets of digital data from social media, web-browsing trails, surveillance and sensor systems, transactions and online orders to illuminate trends and patterns is not uncommon.

Big-Data and Data Analytics have made a significant contribution to serve the risk management requirements of organizations.

Here are 6 ways data can help companies stay safe!

1. Identifying Churn and Reducing Customer Defection: Using Big Data, Predictive Analytics can look into historical data to identify potential churn.

American Express uses historical transactions and 115 variables to forecast potential churn. They believe that now they can identify 24% of accounts (in the Australian Market) that will close within next four months.

T-MOBILE has integrated data gathering tools across its IT systems to combine customer transactions and data interactions to predict customer fluctuations. They claim to have cut customer defections by half in a single quarter by leveraging transaction data from internal CRM and Billing systems and (big) data on social media usage.

Big Data has the ability to illuminate trends and patterns that would have otherwise been invisible, which then creates questions and inquiries into how the business works. Ultimately, the outcome of such pattern identification is often the ability to predict when a certain business-contextual event is about to happen, and then to adjust accordingly in an automated fashion. – Daniel Miessler, Specialist, Creative problem-solving.

2. Identifying Potential Fraud: Big-Data can be put to use to detect frauds which could take hours of manpower and numerous interviews to zero-in on the likely source.

American International Group takes structured and unstructured data from claims databases and handwritten adjuster notes to identify potential frauds. They use charts and visualizations to give insights to the teams. They also use machine learning to make improvements.

BDO (national accounting and audit firm) uses big data analytics to identify risks and frauds during audits. Because of big-data analytics, the process to identify discrepancies is more streamlined and filtered. BDO Consulting Director Kirstie Tiernan noted in one case, that they were able to narrow down the list of thousands of vendors to a dozen and then could review data individually for inconsistencies, making it easier to identify a specific source. 

IRS has been able to recover more than $2 billion of taxes and stopped billions of dollars in fraud, especially with identity theft. Using Big Data, IRS can now prevent frauds, identity thefts, improper payments and ensure compliance with tax laws and rules.

3. Reduce Employee Attrition:  Companies like Xerox, AT&T and Kelly Services have cut their attrition rates by using the services of EVOVL. EVOVL helps in making better hiring and management decisions by applying predictive analytics to more than 500 million data points like unemployment rates, social media usage, etc. to forecast employee churn.

4. Adapt to change: A good business is one which can react to change and adjust its plans according to market conditions thereby mitigating risks.

Procter and Gamble had integrated vast amounts of structured and unstructured data across R&D, supply chain, customer-facing operations, and customer interactions using both traditional and new (online) data sources. Armed with this information they can evaluate their business program success and react quickly to changing market conditions.

5. Reduce risk for new business: Big Data can help predict whether setting up a business at a particular location or for a particular target group will be viable or not.

For eg, The popular coffee-house chain Starbucks uses Big Data to determine whether setting up a branch at a particular location would be fruitful. This decision is based on information like location, traffic, area demographics and customer behavior. This assessment helps Starbucks to make nearly accurate estimates of success rates and thus choose locations based on the propensity toward revenue growth.

6. Financial Risk Management: Assessing risks across the organization and industry is essential for financial organizations to provide risk-free financial services, improve customer satisfaction and it’s also good for the business continuity.

Big Data allows organizations to rapidly bring together multiple data types across silos of data sources to better analyze things like credit risk, market risk, operational risk, compliance risk and asset liability risk. – Raj Kushwaha, CTO, Warburg Pincus

Depósito Central de Valores S.A. (DCV) a Chilean financial firm implemented risk analytics from IBM to its data to pinpoint current and future risks across its business. DCV was able to monitor inherent, residual and concrete risks within the company. Using predictive modeling the company can now prepare for future incidents that threaten business continuity and assess risk for all its applications.

To improve financial risk management, Morgan Stanley, launched a Big Data program to enhance analysis of its portfolio’s size and results using pattern recognition.

Singapore’s UOB bank too brought into picture a risk management system that uses Big Data to streamline the calculation of the total bank risk, reducing calculation time to a few minutes as compared to the earlier 18 hours.

Kreditech, a German company uses Big Data (location data, social media analysis, online purchasing power, and web analysis) for risk management. Using this data, they credit-score individuals to predict individuals that are likely to default.

Kabbage, an American loan company also analyzes the risk of pre-financing and lending working capital to corporate customers. They grant loans based on external data that stems from social media, sales platforms like Amazon and online delivery services.

Paymint is using Big Data to recognize fraud patterns to combat credit card fraud. Their software analyzes several million transactions on a monthly basis to manage risk.

Having access to large volumes of historical records, previous transactions and customer and client information that Big Data storage and analytics provides, allows an organization to identify patterns and trends like never before. This approach then allows the organization to predict and plan for previously unforeseen eventualities and disruptive events that would not have been identified by traditional means. Big Data analytics allows organizations to prepare for incidents based on the information that can be mined from the large volumes of historic and real-time data. – Paul Oughton, Security Consultant, Advent IM.

As Oughton puts it, Big Data helps organizations identify patterns and trends, but organizations must have a robust management plan in place to be able to deal with a disruptive event in real time.

The most frequently used Big Data applications are predictive models to prevent fraud and monitoring and analysis of user behavior for risk management. Big Data analytics gives companies the ability to look into the future and is now being seen as an efficient way to mitigate risks and better serve the clients.