Our goal at Axis Corporate is to make Big Data relevant and utilize it to flow through the client’s systems so that it can provide the necessary monitoring alerts for clients, consumers and advisers. To be successful in identifying Big Data technology, organizations need to remember the two drivers: the amount of transactional data that an organization has and the skills and capabilities in analytics.
My previous article entitled: Understanding Big Data in Financial Services, I provided an overview of the definition of Big Data, its evolution and reality. Now let’s look at concrete examples of how Financial Service companies have implemented big data.
Banks interact with their customers frequently and therefore obtain an abundant amount of both structured and unstructured data. It used to be that data collected from customer service, the online banking portal or snail mail was too overwhelming. Today, technology has reached a level where banks can analyze all their data and comprehend their customers better. The analysis can further help banks to create new products and develop personalized services, thereby adding value to the company increasing data in their own risk models in order to enhance the performance in the risk portfolio.
How have Financial Services companies successfully identified Big Data’s potential?
1. BBVA analyzed payments and ATM cash withdrawal data from 100,000 clients before, during and after a hurricane. By analyzing the financial transaction data, the bank is able to provide critical insights to understanding the economic resilience of people affected by natural disasters. This analysis allowed BBVA to:
- Establish a data-centric internal culture
- Apply the methodology to other countries
- Load-test their analysis platforms
- Obtain a better preparedness for unforeseen events
2. Wells Fargo partnered with SizeUp to compare customer’s business to competitors, mapping customer´s competitors, customers and suppliers and lastly, finding the best places to target your next advertising campaign.
3. Traditional credit-score systems use less than 50 data points to determine creditworthiness. Some alternative lenders calculate it based on more than 10,000 data points. Among those data points, we can find:
- Whether a potential customer has contributed software code to digital code libraries like GitHub (Affirm)
- How a customer fills out a form or navigate a lender’s site (Zest)
- Analysis of the borrower´s text messages and call log. Battery recharging cycle (Lenddo)
- Contacts in the customer´s phone who are trusted borrowers (Branch)
- Frequency the customer calls the parents (Tala).
There are three areas where the use of big data can have a bigger impact:
- Commercial activities: Data helps define the best campaigns focused on the right customer at the right time, resulting in better more customized service
- Alerts monitoring: Data lets the company get a comprehensive and total understanding of that what is happening into the company´s systems so problems can be detected and fixed
- Security: Data gives the possibility to detect any suspicious activity or any different unusual behavior of the customers on the online activity, credit card and ATM usage
Big Data provides huge insights to their customers:
How has customer behavior changed over the past few years? Today, only 7% of the transactions are done through a physical branches. That means that 93% of the transactions are going through an electronic channel.Customers are dealing with technology every day – in the car, on their mobile devices, on TV. Banks should consider incorporating all these new technologies into their services and into the banking offers by digitising their current processes– without compromising excellent user experience into the branch channel.
How can Financial Service companies Unlock the Value of Big Data?
Axis Corporate´s core belief is that companies need to change the way they do three key things:
- Data usage: The company needs to build a culture that continuously seeks for new opportunities and ways of using data. In parallel, the organization needs to convince their clients that they are properly using their data.
- Data Engine: The need to build an efficient and scalable platform that can be used by everybody across the organization
- Data ecosystem: Tomorrow´s company does not need to leverage only its own data but also needs to be relationships and partnerships with external entities with which it interacts continuously and with whom interchanges data either giving out or taking in data.
Guillermo Torres, Consultant in USA.
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