"Big Data" in Banking

Image courtesy of Blogging4Jobs

The notion of "Big Data" has been circulating for quite sometime now. Nowadays, commercial and investment banks are more interested in acquiring, managing, and analyzing data so they can be better equip themselves for a more competitive and risky environment. Big data analytics require high-skilled scientists and personnel, as well as very powerful large machines, to manage large-sized data sets. Many think that big data management is one of the major trends in the financial services industry (they are absolutely right!). To gain competitive edge as well as to mitigate risk, banks need to allocate time, money, and energy to this area.  A lot of talk has been surfacing in the news about how banks are managing risk. Some banks would like to see a convergence between audit and risk management  others still prefer to have autonomous departments. But the truth is, data management can be the key solution for risk management going forward. Managing data through a pure scientific approach (what is called Data Science) , could provide a better way for creating different scenarios for different group of customers and asset classes.

The attached video might sound a little bit technical and doesn't deliver a clean message to people not familiar with the subject matter, so I will try to summarize it in few sentences.  The main takeaways are (1) banks have to take advatnage of open-source data by creating data centers or through outsourcing (2) working with regulators and government officials will be key in acting, and possibly commoditizing, collected data sets  (3) embracing cloud sourcing (4) paying more attention to data as a service that can be acquired from third-party providers.

With all that being said, banks are among those privileged to have easy access to data through various routes. Commercial banks have enormous data from their customers (mortgages, credit cards, loans, deposits, salaries, etc), treasury operations with the interbank, wealthy individuals and corporate customers. So these banks are sitting on a big sets of data mines to the extent they don't know what do with this influx of information. According to the attached graph below, financial and insurance companies score HIGH on the easy of acquiring data and HIGH on the value derived from big data management. According to a study by "The Digital Universe", available unprotected data comprise 80% of all data circling the internet and other networks  but less than 1% of the world's data is analyzed and managed. The report also highlights the increasing fear of data insecurity because the rate of data creation is unproportionately related to data protected; the study suggested that 35% of the analyzed data is seriously endangered and unprotected.

Photo acquired courtesy of Chuck's Blog

The traditional approach of using data to mitigate risk entailed the following:

• Creation of large warehouses that imported data using a specific format  
• Constant recalculation of data to reach a good estimation
• Employment of different risk systems and different formats for each
• Optimization is needed for each format 
• Lack of flexibility inability to accomodate different risk management systems, which means there is alway a need to scale up and create new warehouses. 

Problems that the proper usage of Big Data can help fix includes: 

• Storing, analyzing, and managing data in one large system rather than multiple silos. 
• The occurrence of data analytics without the need for optimizations (system and formate incompatibly).
• Using the data to analyze customers/investors trends and potential risks. Traditionally, systems offered breadth but not depth of analysis. Big data management can tackle both areas more effectively. 
• Hiring high skilled data scientists, and people with a knack for analytics, is a prerequisite to risk management and data management in general. 
• Leveraging data to drive innovation.

According to the article Big Data: The Management Revolution, there are five main challenges management executive (including those in the financial services) will face to make a successful switch to big data management and make the best use out of it: (1) Top executive need to adapt and adopt the data-driven decision making mentality, (2) Talent acquisition, (3) Having the necessary technology, (4) Decision making should encompass multi-functions and cross departments, and (5) Company-culture adoption.  Banks should widely embrace and accept Big Data management and analytics as a source of innovation and an essential part of running their business. The traditional model of storing and using information isn't practical anymore, rather banker need to have simpler "cleaner" solutions at their disposal to make decisions.

-FM