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Over the last decade, financial institutions have increasingly relied on data and data analytics to gain a competitive edge, as well as to minimize their exposure to risk and compliance issues. For instance: institutional investors apply sophisticated data analytics algorithms to historical data and streaming data, trying to find patterns and associations between the data, in order to determine which stocks to buy and sell. According to the Wall Street Journal, algorithmic trading now accounts for roughly one-third of foreign currency exchanges.  When it comes to their own business operations, financial institutions use data tools for tracking compliance, assessing risk, and detecting potential fraud or security breaches.

For these reasons, many institutions are now utilizing data analytics platforms to help them make informed decisions based on contextual analysis of historical and real-time data. An enterprise-class graph analytics platform is a vital addition to this toolset as current methods generally rely on statistical pattern-matching. A graph approach would allow detection of cause-and-effect chains that are apparent by analyzing transactions and stock prices.

Listed below is a subset of the applicability of a scalable enterprise-grade graph analytics platform for financial institutions:

  • Real-time and historical securities trading analysis
    When evaluating a trade, traders should be able to see how the factors at play (currency value, value of other stocks, S&P benchmarks, etc.) compare to historical trends and be able to determine the relationships between various transactions to accurately forecast the stock’s value and identify undervalued equities for investment. Application of sophisticated machine-learning techniques on graphically stored data in real time is the right solution for the problem.
  • Asset management and allocation
    Data analytics (specifically big data) is crucial for evaluating long-term investment strategies. In many cases, assets tend to be tree or graph-structured. A change in one asset can affect related assets either positively or negatively, thereby impacting the overall risk classification of the asset classes. A graph analytics platform lets financial companies manage their investment portfolios by enabling analysis of various assets’ past performance and their predicted growth trajectory while highlighting how they impact the valuation of related assets.
  • Compliance and regulation
    Compliance can be difficult to track for institutions dealing with billions of trades across many geographic regions and industries. A graph analytics solution can maintain a continually-updated database on existing rules and regulations that can impact trading activity, and generate real-time alerts when financial activity is in breach of a regulatory ruling. In addition, financial institutions can also procure and analyze social graphs to catch rogue activities, such as insider trading and securities fraud.
  • Cybercrime prevention and detection
    The financial sector is one of the world’s most heavily targeted industries for cybercrime, with more than 500 million financial records hacked over a 12-month period, according to the FBI. Such security breaches can be devastating to financial organizations and their customers, but many are not identified until the damage has been done. A graph analytics solution can provide an in-depth analysis of activity and transactions to instantly spotlight any unusual behavior or patterns which may be indicative of a security breach. This enables organizations to take a proactive response and shut down unauthorized activity before theft can occur.

The challenge is finding a graph analytics solution that can handle the intricacies of managing each potential use case, while scaling to petabytes of data and performing queries in parallel to data ingestion (cybercrime, fraud detection, algorithmic trading, etc. depend on the ability to ingest streaming data while continuously querying data and relationships stored). While there are many platforms available, few of them offer real-time data analysis at scale, enabling organizations to capture streaming data and analyze it in relation to historical and contextual data to identify opportunities and risks across a broad array of use cases.

Objectivity’s ThingSpan offers a sophisticated data integration solution with a massively scalable graph platform, which is well-suited to the needs of financial organizations. Compatible with the distributed open-source data management framework, Hadoop, and utilizing Spark DataFrames for real-time streaming ingest, ThingSpan has the analytical power to analyze real-time financial and IT data in context. This enables financial organizations to capture powerful insights around data relationships to make better-informed decisions in financial trading and asset management, avoid regulatory and compliance problems, and remain vigilant about cybersecurity attacks and financial fraud.

 

 

Amit Rawlani

Director of Business Development

amit

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