Data Engineering

Post-crisis regulation coupled with cyclical and structural changes, like Swap Execution Facilities (SEFs), means financial services businesses are having to rethink how they capture and leverage data to remain profitable and relevant in an ever-changing landscape.

To meet these challenges, firms are fast turning to big data analytics frameworks that are capable of handling large volumes and varieties of data and producing real-time or ‘perishable’ insights, which can be acted upon before the value of the information goes stale. This is not to say more traditional analytics or business intelligence (BI), typically gleaned from historical datasets, is any less important, however. Hence, big data tools need significant storage capability too.

With the advent of such technology and profound changes to market structure (referenced briefly above), the line between the sell-side and buy-side is blurring. Previously, the buy-side took a longer-term view of assets and engaged with the sell-side to execute orders. Now, however, they can engage directly with electronic venues, meaning an increasing focus on real-time or stream analytics. Other buy-side firms are also measuring execution quality, enabling them to scrutinise routing policies and prove best execution, once the preserve of brokers.

Equally, the sell-side are ramping up their efforts to both maintain existing and find new revenues streams as return on equity (ROE) wanes. Case in point, big data use cases in financial services, client retention and support were flagged as key priorities for such firms. While these strategies take a variety of forms, one common approach is IT Operations Analytics (ITOA). ITOA helps IT teams to better manage IT capacity, spot anomalies in system performance and remedy often lengthy and costly IT issues far quicker than was previously possible. As a result, firms can better mitigate operational risk and, ultimately, better serve clients.

The wholesale financial services industry, spanning buy- and sell-side, exchanges and vendors, is experiencing massive change and challenges. A combination of regulations, market conditions and technological innovation is creating a perfect storm of disruption and opportunity, forcing significant change on business models and operating practices of all participants.

To meet these challenges, firms must be able to find patterns in incredibly large volumes and variety of data, before the value of those insights becomes outdated, i.e. in “real-time”. While in the consumer markets real-time can mean a few seconds to a few hours, in financial trading, one second is way too long.

With expanded customer interaction channels, the avenues for fraud have multiplied exponentially. Fraud patterns continuously evolve making it extremely difficult to pinpoint and proactively prevent such behavior. The challenge that most financial institutions face is to quickly ingest, correlate, analyze and act on the insights uncovered from massive volumes of rich and disparate data streaming in from a multitude of sources so that they can engage their customers more effectively, identify new business opportunities and proactively prevent fraud. Traditional business intelligence (BI) and data warehousing approaches that rely on persisting data and bulk analysis introduces far too much latency to be able to deliver insights in a timely manner.

Operational Intelligence

Operational Intelligence (OI) uses an event-driven architecture to tackle streaming Big Data, delivering real-time analytics that can help detect customer-affecting issues, even before they happen. Examples of data sources that it can tap into include data that resides in traditional data warehouses and other batch-oriented Big Data Analytics stores as well as live streaming data including Web application data and IP address data, location-based data, device data, customer profile data, demographic data, billing data, usage data, as well as social media data.

By correlating and analyzing these streams of data, Vitria OI provides financial services firms with continuous, real-time operational intelligence that puts them in a better position to improve their customers’ experience, engage in real-time one-to-one marketing to cross-sell and up-sell the right offers at the right time, and proactively detect and prevent fraud.

It helps financial institutions connect the dots by tracing live transactions and business activity across interaction channels, systems and organizational silos. It can immediately alert teams to issues and trends that might impact a customer’s experience, ideally before the customer becomes aware of them thereby preventing customer dissatisfaction and churn.

Real-time customer experience management

Patterns of fraud continuously evolve making it extremely difficult for financial institutions to pinpoint and proactively prevent such behavior. As financial institutions grow through acquisitions, it becomes even more difficult to track transactions that span business lines and geographies. For example, transaction scoring logic needs to be continuously updated to take into account newly identified fraud patterns so as to be able to detect credit card fraud at the point of sale in real-time or wire fraud prior to releasing funds. By correlating a customer’s interactions with various products and channels, transactions that are inconsistent with normal behavior can be immediately flagged for review. Likewise, comparing claims with identified fraud patterns can help flag suspicious claims. By immediately alerting the right individuals to potential fraud, investigation teams can be deployed more effectively without wasting huge amounts of time and money on false positives.

OI can assist with identifying and preventing wire fraud, money laundering activities and other suspicious transactions, while it still counts. For instance, the solution can help monitor for spikes in cash withdrawals from ATM machines or for sudden increases in purchase activity across geographically dispersed locations by the same account and immediately alert investigation teams to potential fraud in progress. Rules can be configured to detect and alert teams to sudden surges in money transfers between accounts, in real-time, so that they can immediately investigate these transactions and quickly shut down fraudulent activity.

Real-time one-to-one marketing

Marketing teams for financial services firms are constantly looking for innovative ways to increase the relevance of the offers that they provide their customers. In the past, the various lines of business within banks have operated as silos and so it has been hard to get a consolidated picture of customer’s account, propensity to marketing offers, as well as the channels that they interact with the most. Very often, banks struggle with their existing analytics solutions’ inability to deliver insights that can help promote the right products and competitive offers to the right individuals – while their representatives are interacting with them.

However, banks are beginning to recognize that there are tremendous opportunities from correlating a customers’ account and profile data with real-time transactions, for instance, to be able to make personalized offers in real-time – which result in better customer relationships and increased revenue. Some banks are considering the use of streaming analytics technologies to correlate customer profile data with their online journeys to be able to interact with them online and offer targeted, personalized advice. As banks look at increasing their wallet share, the ability to cross-sell products and services in real-time is becoming increasingly important. Streaming analytics eliminates the latency inherent in analytics using a traditional data warehouse approach and affords banks the opportunity to personalize cross-sell offers in real-time.

Real-time event-based marketing is a new way of engaging in one-to-one product marketing based on recent transactions, where specific customers are or how they are using online financial services apps provided by the institution. It relies on real-time analytics so as to be able to deliver immediate and relevant marketing offers, resulting in a win-win for both financial services firms and their customers. For instance, a financial services firm can use Vitria OI to correlate a customer’s current location (identified through a credit card transaction or activity) with profile information, their propensity to act on a marketing offer, segment information, risk factors and opt-in information, to serve up a targeted marketing offer on their mobile device in real-time. By capitalizing on their ability to readily access and act on a customer’s profile and real-time location data, they can also serve up personalized offers to customers on behalf of their retail partners. For instance, by correlating customer, device, demographic and location-based data in real-time, a financial services firm can determine when a customer is close to a retail partner’s store and then text the customer discount coupons for use in that store. Customer acceptance of the offer can then be analyzed to provide retail partners with greater insight into their customers’ buying behavior and even engage in a revenue-share model. By being able to target customers in real-time using Vitria OI, marketers are better equipped to personalize offers and provide them with genuinely useful information, at the time that it is most relevant to them.

By leveraging continuous analytics, Vitria OI can deliver insights that help a bank’s customer-facing personnel proactively engage in one-to-one marketing.

Here are some examples:
• Recommend appropriate mortgage promotions when a customer makes a payment
• Offer insurance when a person changes address
• Suggest bill consolidation when a customer makes credit card payments
• Present tailored investment opportunities when a customer makes a deposit
• Monitor for retention risk patterns and make the right offer to reduce churn

Real-time customer experience management

Financial services firms are constantly looking for ways to deliver a much more personalized experience, especially to their high-value customers and accounts, in real-time. Some financial services firms have set up special investigations teams to monitor the service levels that these important customers receive. With Vitria OI, they can easily configure KPIs and alerts to proactively monitor individual experience and ensure that group-based service level agreements are being met.

They can automatically kick off corrective processes when established thresholds are met or exceeded. With Vitria OI, these teams have real-time access to information on how individual customers experience a myriad of products and services. They can quickly prioritize responses to those customers that are most likely to churn and provide them with a superior customer experience so as to entice them to stay.

Vitria OI also helps financial services firms capitalize on opportunities to present relevant offers and information to their customers in real-time – for instance, more targeted consumer product offerings, personalized transaction services, and optimized fraud prevention and credit risk alerts.

Real-time Trade Reconciliation

For investment and asset management firms and custodians that perform trade clearing, there is a need for real-time visibility into the end-to-end trade reconciliation process. Reconciliations are a key risk control and investment managers and custodians need real-time visibility to reduce their exposure to risk. It is often difficult to match and reconcile trades from different systems which results in duplicated or missed trades.

With greater regulation and compliance demands, there is a need for increased transparency and the ability to continuously monitor the progress of these reconciliation processes so that exceptions can be immediately flagged and the right individuals alerted to intervene and mitigate the risk. Vitria OI can continuously monitor the progress of these reconciliation processes against established risk indicators so that teams can be immediately alerted to issues.

Unstructured data in the form of stock feeds, twitter feeds, and blogs can impact trades. This data is used by algorithmic traders to produce some alpha from the news. Vitria OI can also help analyze data from these sources to help traders make far more informed trade decisions in real-time.

Complementing Hadoop And Other Batch-Oriented Big Data Analytics

Financial services continue to amass vast volumes of data. Using Big Data frameworks such as Hadoop, these large data sets provide an incredible opportunity for greater insight and intelligence on business operations. It helps financial services firms profile and segment customers better, personalize service offerings, conduct deeper analysis and improve decisions.

However, this analysis is performed on Big Data ‘at rest’. By combining these ‘at rest’ analytics with the streaming data analytics financial services firms have a formidable set of tools with which to tame the data deluge for both Big Data ‘at rest’ and Big Data ‘in motion’. OI provides the ability to visualize real-time feeds and take real-time action on significant events. Where the same feeds are stored in a Big Data framework, OI can work alongside the Big Data store, providing the ability to replay past events, analyze those events for patterns, compare past events with the current situation, and employ patterns learned from off-line analysis to real-time feeds.

Using this combined approach, Sales and Marketing, for example, can understand personalized customer behavior, creating marketing campaigns to an audience of one. Real-time insight enables the creation of novel transaction-, location- and time-based service offerings. Product managers can create precise offer bundles based on an individual customer’s preferences, and the success of new product launches can be monitored in real time against the business plan and adjustments made on the fly.