Keeping Up With Big Data: Part II

November 12, 2020

Risk management: five ways Big Data can keep your organization protected

The amount of data available to the financial services industry increases exponentially each day. Financial markets and institutions are increasingly interconnected, which elevates monetary and strategic risk. Fortunately, this creates an opportunity to leverage Big Data in risk management and improve the industry in several ways. Improvements include: more powerful predictive models, faster response times, more extensive risk coverage, and cost savings. Let’s explore – in what areas does Big Data help with risk management?

  • Fraud Management – assists in quickly identifying potential fraud and minimizing losses.
  • Credit Management – provides better predictive capabilities and new data sources to accurately assess customer behavior.
  • Money Laundering – identifies problems more quickly, with the ability to react in real-time.
  • Market and Commercial Lending – allows for better simulations and the ability to predict market and company behavior.
  • Operational and Integrated Risk – provides greater knowledge and control over interactions with clients — setting a global vision in alternative sectors where financial risk can occur.

Opportunities and challenges: what’s stopping you from harnessing the power of Big Data?

Of course, the prevalence and use of Big Data raises new challenges. If it were simple, everyone would embrace Big Data with open arms. What’s keeping financial services from exploring its potential? Furthermore, what are the challenges in implementing a Big Data strategy?

Consider these examples:

  • Lack of leadership support – Senior leaders are often not convinced of the effectiveness of Big Data and not willing to make the required investment to deliver integrated solutions.
  • Company culture – Organizational structures and internal politics often hamper the progress of Big Data. Business units are sometimes uncomfortable with sharing data.
  • Lack of integration – Fragmented business processes and legacy IT systems raise tremendous challenges for Big Data. These systems typically do not integrate well with distributed systems such as Hadoop or Spark. Time and significant investment are required to overcome the challenge of extracting data from old systems and making it useable for Big Data.
  • Regulatory requirements – The Financial Crisis of 2008 caused significant increases in regulatory oversight of financial institutions. Rules are more complex and access to critical client data can be hampered by government regulations, making it difficult to negotiate the maze of regulations surrounding data access.
  • Business silos – Most financial institutions have experienced multiple mergers and acquisitions throughout the years. This has resulted in siloed business models, which decelerates the implementation of Big Data solutions.
  • Data security – Protecting customers’ data adds an additional layer of complexity and risk for financial services organizations.
  • Data quality – There’s a common phrase regarding data as, “garbage in, garbage out”. If the underlying data cannot be trusted, then managers and executives will not trust the resulting analysis. Steps can be taken to improve the quality of structured and unstructured data used for analysis and prediction. Unstructured data can use ontologies, semantic libraries, and taxonomies, while structured data should emphasize the importance of accuracy.
  • Lack of experience – Big Data is a new and emerging area with a high demand for people to exhibit analytical, technical, and business skills to maximize its effectiveness and implement solutions.

So…where do we go from here?

We stand at the precipice of financial institutions and Big Data. As the marketplace grows increasingly complex and competitive, the use of Big Data strategies in financial services organizations becomes critical to continued success and profitability. Nevertheless, the implementation of Big Data is not without its drawbacks and challenges. Opportunities to further leverage the power and availability of data will continue to broaden the capabilities of financial institutions. Furthermore, it will enhance their ability to remain competitive in fast-paced, increasingly global markets.

If you’re ready to delve into your organization’s Big Data, we can be a partner to help you navigate the process. Send us a message if you’d like to learn more about how we’re helping financial institutions (and organizations in other industries) stay competitive, leverage insights, and ultimately, drive business and customer value.

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Reggie Moore

Data Technical Director

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