Use TIBCO Spotfire® in FSI to empower your users, with need of minimal consulting assistance if any, to own and manage business processes in, among others, the following areas:
Crime Fighting and Claim Management
Find the needle in the hay stack. Explore your data to find outliers that require investigation, e.g. out of hours transactions or unusual communication patterns between team members. Empower your workforce with tools that allow them to encode their knowledge into efficient crime traps. Put machine learning in their hands, from easy to use dashboards, to grasp that knowledge in ways that can be deployed in real time, ensuring optimised true detection rates. Apply to important use cases, such as Busting Financial Crime, be it money-laundering transactions, credit card fraud, trade surveillance, credit worthiness, or quite simply claims that need special processing, e.g. insurance claim management.
Figure 1. Create your dashboard as a Spotfire guided app that walks your user, with easy to use dashboards, through the steps of encoding their knowledge into crime fighting traps that can be deployed in real time with the click of a button
Compliance and Risk Stress Testing
Comply efficiently with ever changing industry regulations. From BCBS's to Dodd-Frank, from MiFID to IFRS, a common backdrop to the extensive regulations imposed on financial institutions is that they possess adequately detailed information that ensures: consumer protection, transparency, and adequate assessement of financial risks, probability of losses and investment portfolio valuation. Upon this, Spotfire can help in several ways:
Spotfire puts in the hands of business users the ability to combine any number of disparate data sources and wrangle it to find desired outputs. Spotfire keeps track of all calculation steps, such they they are always traceable and, more importantly, repeatable. Being a very visual environment, results can include not just required data but metrics of data quality.
Figure 2. Example of the source view diagram of a Spotfire table. Every transformation step is dynamically kept track of and will be reapplied upon data refresh.
Timeliness of reporting
Once a report is built in Spotfire, opening it is all that is required to update it with the most recent information. Spotfire reconnects to all used data sources and applies precisely the same calculation steps, so users can see yesterday's report with now data with the click of a button.
Inbuild sensitivity analysis and predictive analytics
Be it what-if analysis, Monte Carlo simulations to be run on a grid computing environment such as TIBCO GridServer, or advanced statistical models, Spotfire not only lets you see today's valuations but also what their value would be if their guiding parameters, e.g. risk free interest rate, were different. Spotfire allows calling calculations in Spotfire Expression Language, TERR (TIBCO's R), open source R, SAS, Matlab, KNIME, Python, C++ via GridServer, H2O or Spark. These calculations can be as simple or as complex as you require them to be. For example, it can be your credit manager inputting new market information about the specific credit rating of a company to see the impact that has on portfolio valuation. Or it can be your CEO sliding a bar to input his/her beliefs regarding the macro-environment: this value be passed into the valuation of all assets, recalculating their value differently per each asset category, and bringing the final result back to Spotfire for your CEO's appreciation. Check out this simple example of using Spotfire to measure operational risk, our Template on the Community site.
Figure 3. What-if analysis: how does a change in the scoring of a holding affect the portfolio risk exposure
Figure 4. Monte carlo simulation using Spotfire and TERR to generate a sample of likely changes in macro-economic environment, consequent estimated loss distribution of a dependent asset and economic capital needs measurement (blue area of the bar chart)
Shared Best Practices and Increased Transparency
Spotfire reports and dashboards can be shared over intra or extranet. One same report updates with only the data the signed-in user is allowed to see. Your customers can share a report of the value of their holdings NOW just as your business, even internationally, can share dashboards that encompass best practices in any field, from assessing customer credit worthiness to investigating transactions or viewing economic capital requirements. And if the CEO logs in, he can get a view on the fully aggregated international position from the same dashboards your people used in their micro decisions, drill-down to any levels of interesting behaviour, comment, and drill-back up. An international American bank found an increase of 80-90% in productivity best practise dashboards that can be aggregated up natively from this aspect alone.
Figure 5. Sharing dashboards increases transparency and productivity. Notice how one dashboard can give a row-level or aggregated view
Cross-channel Customer Relations
use Spotfire and its in-built TERR engine to understand churn globally or in specific bank branches, create cross-sell campaigns that are relevant to the customer tastes in specific regions, finding the pricing policy that better weighs any customer's risk with their price elasticity. Spotfire leverages machine learning from easy to use dashboards such that you can be assured your company will make the best micro decisions at the right time - which is when your customer is actually interacting with you in your branches, website, phone, mobile app, etc... Find out more about our customer analytics offerings here.
The Algorithmic Insurer
The insurance industry continues to undergo significant transformation, with new technologies, business models, and competitors entering the market at an increasing rate. To be successful in attracting and retaining the most valuable customers in this environment, insurance companies must become smarter and increase the speed in which they respond to customer demands.
The other items on this page apply also to the Insurance Industry - financial crime detection and claim management, stress testing, customer relations. TIBCO's Pricing Accelerator covers another important usecase.
The goal of the Pricing Accelerator in an insurance setting is to dynamically apply a discount to an insurance product based on customers’ conversion or retention probabilities. Using classical logistic regression model in conjunction with other supervised machine learning algorithms to estimate the likelihood that a customer will purchase or renew an insurance product offering, the optimal discount for the premium is estimated. In actual implementations, techniques like constrained regression can be used to guarantee discounts are balanced across genders and age groups, as required by legislation. The model with the best performance is deployed to support real time price offerings. Model versioning is carefully controlled for auditing purposes.
Blog, "The Algorithmic Insurer: Liberating and Leveraging Algorithms Ready for The Fourth Industrial Revolution"
Webinar, "The Algorithmic Insurer – Increasing Agility through Real-Time Decision"
More coming up soon
Exploiting Big Data
Spotfire has a consistent look and feel whether users are connecting to text file data, a traditional SQL data store, or a Hadoop/Spark big data lake, which allows users to focus on the business problem rather than the technology behind it. Spotfire in-dabatase visualisations are fast and agile (example). But Big Data can be best exploited for value if its usage is not limited to visualisation. Spotfire allows end users to apply machine learning to Hadoop data so it helps them find value where it lies: for example, which fields matter most for predicting churn? or fraud? or identifying who may be interested in a specific promotion? Check out, as an example, our Big Data wiki page or the TIBCO Accelerator for Apache Spark (presentation video, download and documentation).
Figure 6. Using Spotfire to guide your dive into the Big Data Lake and find the drivers that matter most
Go to Spotfire Wiki Home page