Banking and Investing
The client was looking to improve customer engagement among advisory customers with a process to deliver personalized email driven by customers’ digital behavior and advisory content consumption. The content was not classified which made personalized recommendations difficult.
- We scraped PDF and web pages from its microsites
- We categorized the content using Natural Language Processing (NLP) based on terms and phrases found in the subject matter
- We modeled topic relevancy for each customer and mapped it to the content library
- We scored the content so that the most relevant articles could be recommended to each customer via personalized email campaigns using the matching algorithms
Fulcrum enabled the deployment of a customized content delivery system to increase engagement with high value customers.
The client needed to identify the geolocation data provider - out of dozens available - that best aided their investment strategy.
- Different preprocessing (data capture, cleansing, metric creation) logic used by vendors
- Lack of standardized evaluation criteria
- Lack of data acquisition, load and process governance/oversight
- We developed a set of evaluation metrics (e.g., data consistency, KPI correlations) relevant to the client
- We designed a vendor questionnaire that allowed us to effectively screen the vendors prior to data capture
- We decoded each vendors’ preprocessing algorithms, applied logic to bring consistency for evaluation, and developed analytics modules that evaluate a dataset in 1-2 days
- Lastly, we concisely synthesized information for the client and recommended finalists based on their needs
Fulcrum’s methodical approach and data audit framework evaluated multiple data sources through a consistent lens, quickly and effectively.
A regional bank’s Human Resources (HR) group was seeking a solution to incorporate market and competitive data to improve personnel evaluation and optimize resource planning.
- We integrated external data (e.g., publicly available competitor data, footprint demographics, etc.) with internal data (e.g., staff experience, branch performance, etc.) and developed new data elements on branch-level market share
- We created a deposit growth segmentation to benchmark branch growth and market penetration, controlling as many factors as available, excluding manager talent
- Lastly, we performed branch performance forecasting for each segment
Fulcrum streamlined the ability to identify exceptional managers, which impacted compensation formulas and recruiting efforts, and also enabled optimized resource planning through forecasting.
In reaction to the Mifid II regulation, the client was seeking to increase the efficiency of the research team’s activities and design new revenue models to meet strategic goals through the use of web-analytics. Research Analysts needed to gain readership insights through:
- Improved engagement measurements
- Coordination with sales to identify new opportunities
Due to regulatory changes, the organization needed to shift how it managed the research function’s revenue and costs of unbundled services
- We tagged the research website to capture tracking details using open source tools and in-house data capture to gain readership insights
- We redesigned the website dashboard reports used by the Analysts to highlight critical usage patterns revealed by newly captured web-tracking data
- We improved and streamlined the dashboard reports used by Sales and Management to highlight upsell opportunities more efficiently
Fulcrum improved website visitor tracking and built more actionable reporting aligned with goal setting and decision making.
The client was seeking innovative data-driven solutions to identify clients’ trade interests through the use of predictive models and a machine learning feedback loop. In particular the client sought to:
- Assess and prioritize opportunities for business development
- Maximize the deal values by anticipating the end-clients’ needs more efficiently
- Provide customized offers to strengthen end-client relationships
- We combined internal data (e.g., transaction history, inventory lists, client holdings, etc.) and market data (e.g., interest rates, currency fluctuations, market volatility, etc.)
- We applied predictive analytic techniques to identify clients most likely to be interested in trading specific products in the bank’s portfolio
- We built a user feedback loop to continually update the process to identify better opportunities
Fulcrum effectively prioritized end-clients and provided a matching engine to allows the client to stay competitive in the market.
The client was seeking a streamlined method to organize their disparate data sources into a single location to start generating business insights. Some of their challenges included:
- A massive amount of disjointed information including internal and external data
- The lack of a unified view of the customer because the source systems are managed by different siloed teams
- The internal IT team was backlogged, and analysts needed support testing the latest big data tools
The client partnered with Fulcrum to utilize our Agile Analytics Lab to experiment with big data solutions
- We created reusable data processing pipelines to allow easy data loading into Hadoop and data preparation for analysis
- After establishing the central repository, we then cubed the data using Hive and Spark for fast querying of metrics
- Output tables are stored in HBase and Impala is used for queries to gain rapid response while Tableau is utilized for dashboard reporting
Fulcrum’s lab enables a holistic view of the data to unlock insights and allows rapid testing of various open source tools. Our data experts guide the process allowing the clients to modernize their analytics team.
In reaction to regulatory requirements to retain and classify payment records, the client needed help creating a cash transaction classification system for regulatory reporting and compliance, looking specifically to solve for following challenges:
- Migrating away from using Excel that applied the classification rules with a manual process
- Implementing scripts to allow for processing a greater amount of data
- Needing to improve operational scalability rather than rules being created small chunks at a time
- Improving repeatability, decreasing processing time, and reducing error
We developed a text mining and reporting engine in R that efficiently runs against the client’s full data warehouse
- We automated code in R to be applied against the data warehouse for fast and accurate classification
- We built a front-end UI to allow business users to create and modify rules to refine business logic and improve resulting classification rate
- We created reporting on summary statistics and classification rate, including the impact of the addition of new rules
- We created a next generation text mining process to prioritize the new candidate classification rules
Fulcrum created a scalable and sustainable solution to comply with regulations, which provided significant time-savings from manual labor, increased accuracy of classification, and introduced a mechanism to easily create and integrate new rules.