You have the opportunity to fundamentally change the way you serve your customers using the vast amounts of market and customer data at your disposal. However, as your data grows exponentially in both volume and complexity, effectively analyzing it to wield competitive advantage can prove to be extraordinarily difficult.
Ayasdi’s solution provides a new approach to analyzing and finding insights, anomalies or similarities within large sets of multi-variate data, without requiring large teams of data scientists to write queries or code algorithms. It uses a new technique of topological analysis combined with machine learning algorithms to help your firm tackle some of the toughest data problems. Using Ayasdi’s solution, banks, credit unions, capital markets and lenders can uncover previously hidden insights within their data to help drive stronger customer relationships, increase profit margins, prevent fraud, meet regulatory requirements, and mitigate risk.
Leading financial services firms are looking for ways to precisely segment their customers to help optimize the way they deliver client services.
Traditional customer segmentation techniques are based on information gleaned from surveys, interviews and rudimentary data mining exercises. However, these approaches do not take into account a customer’s actual behavior or evolving preferences.
Ayasdi’s advanced analytics solution has helped investment banks and leading private wealth management firms to create precise and dynamic client profiles. It represents a fundamentally new approach to stratifying clients into groups, in economically meaningful ways. The solution can ingest and analyze client information related to income, purchasing behavior, risk tolerance and engagement to identify subtle patterns and relationships in the data that enable sophisticated client segmentation.
For example, the Institutional Clients Group at a large financial services conglomerate used Ayasdi’s solution to correlate client revenues with the associated costs of serving those clients. This helped the firm create more robust and meaningful taxonomies for their clients. By redefining its customer segmentation taxonomy, the firm was able to focus its resources on high profit customers and reduce its costs to serve low profit customers. This helped increase revenues and reduce operating expenses.
Ayasdi’s advanced analytics solution can also help your firm proactively identify indicators of client disengagement. It employs a data-driven approach to identifying those combinations of client attributes that are most likely to result in specific customers or accounts leaving a firm. By pinpointing subpopulations of customers with high levels of flight risk, your firm is in a better position to deploy appropriate retention strategies. The solution also supports behavioral incentive testing efforts. It can uncover insights that can help your firm assess which client remedies are the most effective at impacting behavior.
Institutional capital markets businesses are under constant competitive pressure to optimize costs while retaining their client base. Traditional methods of segmenting clients have primarily been based on the levels of assets under management (AUM). As trading volumes vary and investment strategies evolve, leading capital markets businesses are in need of more robust ways of segmenting their institutional client base.
Ayasdi’s advanced analytics solution represents a fresh approach to helping institutional capital markets businesses analyze the relationships between market conditions and client revenue. It enables your firm to further segment transactions by product, client type, and asset manager, and correlate transactions with a multitude of market indexes, commodity prices and security prices. By identifying distinct risk segments within market regimes, Ayasdi’s solution helps sales teams precisely target clients according to their product preferences and focus on clients that are most likely to transact, in a specific market regime.
For example, a large institutional capital markets firm deployed Ayasdi’s advanced analytics solution to gain a better understanding of its client trading revenues in relation to developments in the market. Using Ayasdi’s solution, the firm was able to quickly analyze over 29 million records representing daily trading revenues over a five-year period. The analysis also incorporated 100 daily market and economic indicators across a variety of indices, spreads, and currencies. They were able to uncover new insights that enabled their sales team to drive higher transaction revenue and optimize the deployment of research analysts and other expensive value-added services.
Fraud is a multi-billion dollar problem for financial services organizations. These enormous losses point to an inherent problem in the way risk departments currently model and flag fraudulent transactions.
Existing statistical analysis tools do not afford modelers the ability to evaluate data in an unbiased manner. They do not allow them to easily interact with entire transaction data sets alongside fraud model results.
Typically, firms deploy solutions that rank and score transactions based on a variety of models. At issuing banks, for instance, fraud models are either developed internally, or retained from external vendors and transaction processors. This approach has an inherent weakness resulting from the need to correlate results across models from different sources.
To address this shortcoming, Ayasdi’s advanced analytics solution dynamically parses fraud rules and evaluates their efficacy across a variety of models. It correlates input variables across disparate models and statistically ranks features to explain model shortcomings, when compared with the ground truth. It provides firms with a way to supplement their methods of validating the effectiveness of variables used in fraud rules engines.
For example, a leading transaction processing firm used Ayasdi’s advanced analytics solution to analyze its transactions. It detected seven characteristics that when present, simultaneously, increased the likelihood of it being a fraudulent transaction. This insight increased the accuracy with which the firm could detect fraud from 29% to 99%, within a subpopulation of transactions.
The new model protects the firm from incurring financial losses in the form of chargebacks in the future. By operationalizing a validated model using Ayasdi’s solution, the firm can not only adapt its algorithms to accurately detect instances of fraud, but also optimize rules quickly as fraud techniques evolve over time.
The 2008-09 financial collapse increased the pressure on financial institutions to have stringent risk management measures in place. For example, for banks with consolidated assets over $50 billion deemed bank holding companies (BHCs), additional risk assessment frameworks and budgetary oversight are now commonplace as a result of directives from the Federal Reserve.
Capital is central to a BHC’s ability to absorb losses and continue to lend to creditworthy businesses and consumers. To assess a BHC’s financial foundation, the Federal Reserve oversees a number of company-run stress tests. These tests, both qualitative and quantitative in nature, are meant to measure the sources and uses of capital under baseline as well as stressed economic and financial conditions.
Ayasdi’s advanced analytics solution helps BHCs ensure a satisfactory outcome in such tests. For example, a leading BHC has deployed Ayasdi’s solution to help streamline their Comprehensive Capital Analysis and Review (CCAR) process. Using the solution, the firm can rapidly correlate macroeconomic variables to revenue streams. By analyzing these correlations, it can uncover potential variables that closely relate to the underlying performance of a business unit’s revenue.
The BHC’s team is then able to screen variables via statistical tests like stationarity, multicollinearity and explanatory variable assessments to arrive at the best variables to incorporate into their internal models. By facilitating the end-to-end process using Ayasdi’s solution, BHCs are better equipped to gain a satisfactory review from the Federal Reserve.