How the Financial Services Sector Can Deploy Big Data to Win Customer Confidence and Trust

The financial sector crisis of 2008 has brought a lot of attention to the way financial services companies work. Many companies were caught scrambling to comply with the revised set of compliance but the main issue was how to regain the trust of customers that had suffered badly and also reduce risks of credit, default and liability. Big Data has come to the rescue of companies attempting to understand their customer profiles, requirements and aspirations so that they are in a position to develop and deliver products and services that will be preferred by customers. Use of Big Data also allows them to ramp up customer satisfaction levels as data management and retrieval is far faster.

Risk Reduction

Big Data allows companies to create individual risk profiles for all customers based on a number of variables. These include past purchase behavior, social network presence, lifestyle and other public data. The quality of the risk profile improves with the quantum of data available and lowers the risk of credit default. Insurethebox, an insurance company has successfully used Big Data captured with a tiny device onboard the insured car that captures data of the vehicle usage and driving patterns to determine a risk profile that is used to deliver a custom insurance package for every customer.

Enterprise risk management can be considerably aided by Big Data by adding different sets of data to arrive at a client’s risk profile when a loan is requested. The risk profile can take into account multiple factors such as business, earlier claims, managerial lifestyles and investment management considerations that provide a superior picture of the organization’s risk appetite than a mere business plan that is based on unknown and different variables. Big Data usage has resulted in predictive models that are more accurate and sophisticated and significantly assist companies to reduce their enterprise risk. Employing Big Data debt reviews are far more accurate and detailed.

Fraud and Crime Detection

Big Data has also been instrumental in detecting fraud when the data analysis reveals that a customer has deviated from his usual established pattern of behavior. For example, clever algorithms can detect when a particular credit card is being used at locations not close to each other in a very short timeframe, indicating a strong likelihood of credit card fraud. The sophistication of application has progressed to such an extent that possibly-fraudulent transactions can be blocked even as it is being processed at the retail outlet. Visa has successfully implemented a method that analyzes 500 transactional parameters simultaneously to detect and stop fraud. Using Big Data banks have also been at the forefront of stopping criminals intending to rob ATMs. The analytics can reveal which ATMs are at risk so that adequate security measures can be taken in advance.

Customer Satisfaction and Confidence

When the purchasing behavior of a large database of customers is analyzed, a lot of customer behavior can be easily understood and appreciated. With the data at hand, retail financial institutions like banks have the potential of being able to understand their customers far better than possibly customers can do themselves. For example, payment transactions can reveal a lot of information about the preferences of customers. Big Data enables improvement of customer satisfaction in many ways.  Proper alignment and connectivity of internal systems allow availability of all customer information immediately after a customer logs in online or calls the call center. Algorithms that can analyze social media behavior also help in understanding customer preferences and the opinions of industry leaders. Product usage patterns can also be successfully interpreted to reveal customer behavior, and can to a great extent replace expensive and often inaccurate customer surveys.

Cost Reduction and Sales Boost

Big Data can be used to monitor and analyze consumer behavior in real-time. It reveals behavioral patterns that can be used to take predictive action and prepare the organization to get ready with products that fit changing circumstances. The right profile of customers can be targeted so that higher conversion rates can be achieved. Deployment of Big Data also lessens the extent of investment required to be made by finance companies in maintaining legacy systems as the existing data set can be easily migrated. Operational efficiency also boosts when unstructured or transactional data is monitored and analyzed to anticipate future trends and customer demands.