5 Key Big Data Challenges in Banking Industry

Date:   Monday , September 01, 2014

Big Data is the new oil for Banking Industry. It is here to stay. McKinsey calls Big Data \"
the next frontier for innovation, competition and productivity.\" Banks are moving to use Big Data to make more effective decisions. They are tapping into a growing stream of social media, transactions, video and other unstructured data.

Banks have unique insight into how, where, with whom and when customers are spending money-by analyzing such information, Banks can build an insight into customer intelligence and behaviors that they may well be able to monetize. Checking customers\' names against a sanctions blacklist can become highly complicated in a world where a Bank has multiple customers with the same or a similar name. By using Big Data techniques, this reputational risk can be mitigated and managed. Big Data can also be used to enhance account and relationship management. By coordinating the collection of data already in the public domain-such as share price movements, a change in auditors or a director selling shares in his company-and passing it to account teams, understanding of key client businesses can be improved. Further insights can be derived from additional internal data, perhaps focused around the early identification of potential problems-for example, how credit lines are being used against agreed limits; to monitor account crediting behaviors, as problem accounts often credit funds late in the day; and to identify payments patterns of potential interest. Another exciting area for Big Data lies in the potential to create new income streams for Banks. Also Banks can track consumer sentiment, test new products, navigate the marketplace, manage business relationships and build customer loyalty in new and more powerful ways.

Yet Big Data comes with many challenges. It presents a number of challenges relating to its complexity. How Banks can understand and use Big Data when it comes in an unstructured format, such as text or video or how they can capture the most important data as it happens and deliver that to the right people in real-time or how they can store and analyze it, given its size and our computational capacity. And there are numerous other challenges, from privacy and security to access and deployment.

Some of the key Big Data challenges for Banks are detailed below -

1.Legal and Regulatory Challenges
Big Data can come with big legal and regulatory concerns that have complexities and limitations due to sheer size. Many companies already have control and data management procedures in place for small data-and a comfort level that those controls are appropriate. Given the growing impacts of regulation and oversight, Banks are steering clear of Big Data-or at least proceeding judiciously-simply because of the risks.

2.Privacy and Security
Big Data offers great potential to provide major steps forward for Banks, but it also comes with a large red flag concerning privacy and intrusion. The potential for abuse of this data is significant, but Banks need to get it right and \'Big Data\' techniques and analytical tools can help Customers get better service and assist Banks to target resources more effectively. It\'s a fine line between being helpful and intrusive.

3.Talent Challenge
Good talent is scarce. Finding that magic combination of hard science and business acumen is scarcer still. Blending a staff of left-brained data scientists and right-brained visualization teams is a new workforce management paradigm. Big Data Specialists need to have solid business understanding, SAS/R/SQL/Python programming and statistical knowledge along with Visualization skill. Also Banks need to pay significantly more to a Data Scientist or a Big Data Specialist compared to a traditional ETL or Business Intelligence hire.

4.Data Quality
Data Quality is important even in the age of Big Data. The greatest impact of Big Data is on Data Quality. To ensure highest Data Quality and Integrity, Data Quality attributes-validity, accuracy, timeliness, reasonableness, completeness, and so forth-must be clearly defined, measured, recorded, and made available to end users. Artifacts relating to each data element, including business rules and value mappings, must also be recorded. If data is mapped or cleansed, care must be taken not to lose the original values. Data element profiles must be created. The profiles should record the completeness of every record. Because data may migrate across systems, controls and reconciliation criteria need to be created and recorded to ensure that data sets accurately reflect the data at the point of acquisition and that no data was lost or duplicated in the process. Special care must be given to unstructured and semi-structured data because Data Quality attributes and artifacts may not be easily or readily defined. If structured data is created from unstructured and semi-structured data, the creation process too must be documented and any of the previously noted Data Quality processes applied. For Big Data Quality and Data Management, Banks need to create Data Quality metadata that includes Data Quality attributes, measures, business rules, mappings, cleansing routines, data element profiles, and controls.

5.Organizational Mindset
Many Banks are still driven by mostly Past Experience, Intuition, SME knowledge and Customer Experience. They need to have more Data Curiosity, Data Driven thinking and need to invest more in acquiring, storing and analyzing data. The Banks which will take advantage of Big Data will stay much ahead of the others-and especially in this strict regulatory environment. Banks need to have data and facts ready. So a paradigm shift is needed in mentality of the Sr. Leadership and they need to give data investment the top priority.

Lastly, it\'s all about which business question Banks are trying to solve. They need to have specific Business Question or Issue in mind that needs resolution and then they should go after acquiring and leveraging Big Data. Big Data has many advantages but there are many challenges and risks as well as specified above-but associated rewards (some of the major potential benefits were detailed before) offered by Big Data definitely justify the effort.