The financial industry is flooded with data, from retail banking to investment banking: it is no surprise that the BIG DATA and business analytics market is booming. According to the analysis firm IDC, it will be worth US$187 billion, compared to US$122 billion in 2015, representing growth of more than 50% over this period. The proper processing of this data is essential for the proper development of the financial institutions‘ business.
In this article we will mainly deal with the Algorithmic analysis problems that we will raise. Should we have a generic approach to algorithmic analysis? Should we have a dichotomous approach, “Divide and conquer”? “MIF II” a lever for the BIG DATA?
It is at the heart of the “MIF II” regulation, the actors have the obligation to be uncompromising on the quality of their data, we cannot analyse without a good Data base, MIF II has been and will be for financial institutions a key driver in the development of the BIG DATA.
To handle the different channels of customer relations, information systems have often been built on vertical solutions that isolate the data. It solutions remove many barriers to the amount of data that can be extracted, stored, indexed, reconciled and aggregated. They provide power and versatility to find value in data that has not yet been used or is too expensive to process.
Just look at the historical players in the software who mention their natural interfacing with Hadoop (one of Big Data’s flagship solutions) to understand how essential these solutions are becoming. Beyond acquisition, It analysis tools deeply refresh business intelligence (BI) and, consequently, customer behavioral analysis.
They have the ability to query huge amounts of data in near-real time, drastically reducing the overall responsiveness of our system, as well as the ability to perform predictive analysis. Thus, they allow the construction of behavioral models based on statistical models developed and refined with colossal volumes of data (we go beyond manual segmentation, through Machine Learning) and, globally, a democratization of BI tools, which are directly invited in operational applications.
Big Data related technologies not only offer increased acquisition capacity and BI acceleration, but also great flexibility to create offers faster that can handle hundreds of thousands of external and internal requests.
The applications of BIG DATA cover a wide range of fields and allow, for example, to : Measure instrument Hit Ratio in an organized market for campaign transformation of new customized products: BI aligns with new trend rates. Detect fraud in a reactive manner, by cross-checking in real time unusual behaviours (type and amount of transactions, event announced on the media that would justify a trip).
Cross-reference data from historical information on the prices of an underlying, the type of instrument for the different types of financial companies (credit companies, life insurance, mutual funds, hedge funds, M&As, etc.). To capture customer satisfaction, enhance loyalty in a personalized way by weighting by the customer’s ability to influence. The promises of BIG DATA are those of a 360° knowledge of the customer and the business, which is not fixed in a dashboard but directly connected to the offers and the customer relationship.
you are a solution seller in an equity derivative trading room, you mainly sell Fungible notes (Warant EMTN BMTN Certificate etc…), today MiFID II requires all ISPs (investment service provider) to be transparent on their price, a client wants the best price, transparency is put forward the client must be aware of how much the ISP goes from Fees and PnL on an instrument or Trade.
The BIG DATA will be the essential tool for these agents to avoid market incidents that may occur, but above all a crucial element to target profitable business at the Bank. “MIF OR BIG DATA” the two are connected, the democratization of these tools are the future of financial institutions.