Insightful Analytics: Leveraging the data explosion for business optimisation 09 Top Ten Challenges for Investment Banks 2015 Insightful Analytics: Leveraging the data explosion for business optimisation
Insightful Analytics: Leveraging the data explosion for business optimisation The wealth of information and data available to investment banks continues to increase exponentially and, while the scale and frequency of creation presents a challenge for effective data management, it also provides an enormous opportunity for insightful analytics. 09 Analytics can be used to help banks respond to the main drivers currently shaping the industry: regulatory reform, margin pressure, and operational efficiency. Value-adding analytical tools that make greater use of existing big data solutions represent a significant competitive advantage for those banks that can deploy them effectively. The challenge is how to develop effective analytical tools that enable the extraction and visualisation of meaningful conclusions to inform wider business strategy and support day-to-day processing. This also involves getting the right information in front of the right consumers across the organisation, on a timely basis. The data explosion The explosive growth in both structured and unstructured data has captured the attention of capital market firms and vendors, given the variety of valuable information that can be extracted and the myriad of potential uses. Celent estimates that big data spending in capital markets will reach US$1.2 billion in 2013, growing to $2.4 billion by 2015 as firms invest in an effort to leverage the increasingly large data sets for ever more complex solutions. i To date many banks and vendors have focused on existing, underleveraged (mostly historical) data sets, and the first challenge for many is how best to collate and integrate the huge amounts of data now available (see Fig.1): i Big Data in Capital Markets: Expanding the Search for Big Ideas, Celent, 2013 2
Figure 1: Data Preparation Principles Integrate Consolidate data from multiple sources Discover Identify records within a large data store Curate Evaluate and improve quality, trustworthyness & accuracy Align Map data schemes and individual records to a common model Source: Accenture Analytics The right data preparation is therefore vital, but it is only the first step. It is critical that analysts have access to the right tools to cut through the noise in large data sets and identify actionable ideas quickly and often. Demand is now moving beyond platforms designed for routine, industrialised data collection and number crunching, to true data discovery platforms designed to find and visualise the unknown unknowns the hidden insights contained in these Figure 2: From Big Data to Big Outcomes Tagging Patterns Filtering Big Data Discovery Casual Analysis From Big Data Testing Factors & Insights increasingly large data sets. Big data must drive big outcomes (see Fig.2). Smart data cultivated data that can be easily managed and manipulated by business users is one approach that addresses the demand for greater insight. The aim of smart data is simple: to provide quick insight and leverage opportunities to increase business generation proactively, by providing guidance on clients needs based Machine Learning To Big Outcomes Analytics Automation Simulation Execution Decision on prior experience and market conditions. By carrying its lineage and context with it, smart data can also be reused in multiple business contexts. Indeed, it is vital that the analytical, data-driven conclusions gleaned from multiple data sets are made available to all areas of the bank that can potentially use and benefit from them. Certain data sets are in particular demand for banks in response to a specific regulatory pressure or cost-reduction imperative, but the potential applications for business optimisation can range widely depending on the type of data, user groups and the particular business goal (see Fig.3). Adding meaningful insight across the business Within the investment bank, therefore, the potential applications of analytics to support process optimisation and strategic decision making are wide-ranging and varied. While the use of analytics is readily established in some areas, the potential for future developments is significant (see Fig.4). Source: Accenture Research 3
It is critical that analysts have access to the right tools to cut through the noise in large data sets and identify actionable ideas quickly and often. From a regulatory perspective, garnering and scrutinising greater quantities of data on individuals behaviour with smarter algorithms will empower a much more proactive and effective approach to detecting misconduct in the front office. Advancements in technologies such as text mining, computational linguistics and Complex-Event Processing (CEP) are also enabling real-time management of conduct risk on the trading floor, often Figure 3: Data usage in the investment bank Reporting Operations Front Office Management Regulators Clients Sales Efficiency Regulatory Customer Strategy Market Transaction Pricing Risk before a prospective rogue event. Compliance departments can use these tools to monitor firm activities and uncover violations. Similarly, new ways of managing and mining data are optimising the Know Your Client (KYC) and anti-money laundering (AML) controls for detection of fraud and malpractice by clients. The aggregation of market and position data, collateral agreements and risk calculations opens up the possibility for real time what-if analysis to identify the cheapest assets to deliver for collateral management purposes. In addition, new suites of advanced visualisation tools will support better decision-making for collateral allocation and inform the front office with richer information on the cost of collateralisation for optimal order and execution management. Advanced data visualisation tools are also providing additional business intelligence by drawing on data from multiple sources and providing a single interface with new levels of granularity in areas of the bank that have previously been opaque. If banks want to predict how prices of securities will evolve, how demand and supply will change for securities (intraday, weekly, monthly, quarterly etc.) or how risk is correlated, they must find the hidden or often unknown correlations between hundreds and thousands of external data streams. Further examples include post-trade analytics on the The aggregation of market and position data, collateral agreements and risk calculations opens up the possibility for real time what-if analysis to identify the cheapest assets to deliver for collateral management purposes. Source: Accenture Capital Markets 4
Although a real challenge, the integration of data analytics into investment bank operating models is the future state Figure 4: From Big Data to Big Outcomes Analytics Application Example Use Cases Users Trade Surveillance & Financial Crime Discovery and detection of aberrant behaviours for targeted investigation to mitigate conduct risk and potential financial penalties Rogue trading Unauthorised trading Benchmark rigging (i.e. LIBOR/FX rate manipulation) Commodities price fixing (e.g gold prices) Fraud in dark pools Background checks/analysis for client onboarding/kyc Compliance, Onboarding Funding & Capital Management Real-time/On-demand capital allocation and optimisation and liquidity management Decision Suport for the middle Office Complex event processing Client Facing Technology for the Buy-Side Pre-Trade Analytics for the Front Office Real-time Stress Testing Middle Office, Treasury/ALM Business-Led Intelligence Next-generation data warehousing but with a business-led approach Institutional Customer: Pricing for advisors Trade Profitability Client lifetime value / 360 View Cross-sell / Up-sell opportunities Client Retention / Acquisition Management Information: Post-Trade Securities Processing What-if Analysis of Portfolio Risk P&L Trending of Traders & Books Sales & Trading, Middle Office, Operations, Risk Source: Accenture Capital Markets cost to serve for securities processing, and deeper insights on the risk and P&L trends of individual trading books. At the same time, market leaders are making the most of the opportunity to create richer interactions with their clients by providing data and analytics for their trading activity, and portfolio health checks in the wealth management space. The capital markets industry has vast amounts of data and a vast appetite for it. Although a real challenge, the integration of data analytics into investment bank operating models is the future state, and has the potential to bring insight into everyday business-decision making as well as long-term strategic planning. If not yet a market-wide standard, being able to draw factual, data-driven conclusions in order to support analysis of business sentiment or showcase operating trends is becoming a prerequisite for the leaders in the industry. 5
About Accenture Accenture is a global management consulting, technology services and outsourcing company, with more than 305,000 people serving clients in more than 120 countries. Combining unparalleled experience, comprehensive capabilities across all industries and business functions, and extensive research on the world s most successful companies, Accenture collaborates with clients to help them become high-performance businesses and governments. The company generated net revenues of US$30.0 billion for the fiscal year ended Aug. 31, 2014. Its home page is www.accenture.com. Accenture Experts To discuss any of the ideas presented in this paper please contact: Matt Long Managing Director, Capital Markets, London matthew.j.long@accenture.com +44 20 7844 3136 Edwin Van der Ouderaa Managing Director and Digital Lead Financial Services Europe, Africa & Latin America edwin.vanderouderaa@accenture.com +32 22 267 312 Copyright 2014 Accenture All rights reserved. Accenture, its logo, and High Performance Delivered are trademarks of Accenture. Disclaimer This report has been prepared by and is distributed by Accenture. This document is for information purposes. No part of this document may be reproduced in any manner without the written permission of Accenture. While we take precautions to ensure that the source and the information we base our judgments on is reliable, we do not represent that this information is accurate or complete and it should not be relied upon as such. It is provided with the understanding that Accenture is not acting in a fiduciary capacity. Opinions expressed herein are subject to change without notice.