In the era of information, the bank with the most actionable insights is the winner. The age of “big data” has begun to slowly filter through into the banking environment, with banks being able to design customer-centric solutions using statistical models and information gathering techniques.
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So, just, what is big data? The term refers to large amounts of complex information, which often contain a variety of facts, generated at high speeds, and can sometimes be inconsistent. For example, your bank has data on the products that it currently holds for you, along with your transaction behaviour.
The amount of data out there continues to grow exponentially, with the International Data Corporation (IDC) forecasting that the global volume of data will increase from 130 to 40 000 exabytes (40 billion terabytes) by 2020. Big data is important for financial institutions as our customers have ever increasing demands from us. To acquire and retain customers, teams of individuals, called data scientists, have to develop means of optimising revenue for the bank as well as the customer. These data scientists sift through large amounts of information to offer meaningful insights that can guide, and sometimes drive, business strategy.
To ensure accurate insights, one must be aware of best practices for managing big data in the financial environment. For example, analytics departments should focus on key business problems faced by their employer to maximise the effort and resources given to solving a problem. Perhaps the worst hurdle to overcome relates to data integrity – often banks do not have accurate data on their customers as much of this is user inputted, leading to basic errors which cause complex problems.
Additionally, the speed at which data is analysed and interpreted is essential as there are a number of competitors going through the same exercise. Here, it is often the case as the first to implement a solution is the winner. Lastly, to ensure robust prediction models, one should allow for a variety of data to be used to stress test those models in a practical setting.
How do banks derive value from big data? First of all, the ability to understand your client is crucial. Big data allows more personalised approaches to selling products that are best suited for the customer and are timed correctly. This translates to higher acceptance rates, streamlined infrastructure costs and increased customer satisfaction and bank profitability. Secondly, being able to proactively resolve service delivery issues remains critical for a bank to minimise resources and costs.
Big Data enable banks to deliver performance management platforms to drive productivity and service delivery at operational centres. Lastly, being able to understand the customer allows better credit risk management – ranging from granting of facilities to mitigating fraud.
While 63% of banks globally recognise the competitive advantage that Big Data provides, only 9% can actually capitalise on the area by employing the right skills sets. In South Africa, this number is much lower, with only 38% of corporations across all industries having achieved a competitive advantage of using big data.
While many large corporates in South Africa have the infrastructure in place to handle big data, a survey by Strategy Worx shows that many of these organisations do not use dig data and analytics in any significant manner. According to a survey by Microsoft and Celent in 2013, only 37% of financial banks internationally have practical experience in using big data. This number is considerably lower when one considers banks in emerging economies along with many traditional banks adopting silo approaches instead of pooling data from the entire organisation.
Studies have shown that banks that effectively use Big Data to focus customer analytics show a four percent gain in market share over competitors. The first bank to get it right in South Africa therefore has a lot to gain.