Discovering value in large amounts of information in a timely manner and strategizing based on the insights gained should be the cornerstone of a successful financial organization. Traditionally, financial institutions have keenly embraced the use of quantitative methodologies in activities ranging from pricing, risk management, portfolio optimization, and algorithmic trading including its recent iteration high frequency trading. In order to work proficiently, financial models and algorithms must be tuned based on the most up to date and comprehensive real world information. This is where the future certainly belongs to big data technologies. A few possible applications include:
Text analytics augmented trading. Traders many a time trade on "news", relying on intuition and experience to gauge the implications of a news item and formulating a trading strategy; often going wrong. In contrast, big data text and predictive analytics capabilities offer more timely and surer trading strategies after processing hundreds and thousands of news items on a particular segment for meaning within seconds.
Big data analytics for risk determination. A radical improvement in the ability of financial institutions to predict major events that could potentially be catastrophic and best avoided through big data processing of news and numbers preceding these events.
For retail banking institutions, big data can prove to be extremely useful in several scenarios like:
Fraud detection and prevention. Recognizing emergent patterns of fraud through real time analytics of debit/credit card transaction data.
Tailor made banking solutions. Developing detailed customer profiles through the application of analytics on data aggregated from various channels including transactional, social, mobile, call center and website clicks for the purposes of tailor made banking solutions.