Case Study

Maximizing Lending & Minimizing Risk for a P2P FinTech

Published July 20, 2022

High-velocity staffing drives a holistic data-management strategy.

A growing FinTech partnered with Altimetrik to implement a world-class data platform to maximize loan originations, minimize risk provisioning, and build a highly scalable data platform.

Over the first four months of the project, Altimetrik ramped up an 83-member team including six data-engineering scrums, two data-science scrums, three product-engineering scrums, and four Salesforce-engineering scrums.

40% Higher Scrum-team Release Velocity

36% Improvement in Risk-assessment Accuracy

After 4 Months: 40 Hires per Month in 7 Tech Stacks

8 Distinct Classes of Skills Staffed with Industry-leading Talent

A holistic approach and speedy team ramp-up drive positive business outcomes.

While some teams within the existing IT landscape and operating model were familiar with the agile approach, others were struggling—or simply maintaining an outdated SDLC. Some elements of a mature DevOps shop were missing, hampering the effective unified system needed for higher project velocity.

From a data analytics and data science perspective, no solutions or proven use cases had been established. Altimetrik took a holistic look at the data platforms, the approach to data management, and the use of data for business outcomes. Altimetrik proposed to drive agile maturity and fill in the missing pieces of DevOps (mainly QE automation) throughout the enterprise.

Altimetrik ramped up a 20-member team in the first month to get the project off the ground and quickly added another 63 team members in the following three months for a total of six data-engineering scrums, two data-science scrums, three product-engineering scrums, and four Salesforce-engineering scrums.

Outcomes Delivered

Maximized Loan Origination

Optimized the discoverability and ease of customer acquisition.
Intelligent analytics-enabled highly tailored loan products designed for the needs of the business-borrower segment.

Minimized Risk Provisioning and Maximized ROI

Assessed risk through machine learning to minimize the occurrence of bad loans or payment delinquency.
Clarified and simplified visualization capabilities to facilitate interest-rate decisions (pricing), predictive-uptake models and what-if analyses.
Improved accuracy of direct mail targeting by 36%.

Globally Seamless Commercial Ecosystem

Utilized a highly scalable platform tailored for globalization, multiple regulatory environments, operating processes, and business cultures.

“While there is some value in analytics being done by a small team sitting in an ivory tower, the real promised value of analytics for a company comes from immersing analytics in the day-to-day operations. By making sure decisions —small and large—are made with pragmatic analytics drawn from data, augmented by both the expertise and gut feel of the decision-maker.”

Jayaprakash Nair

Head of Analytics

Digital Business Payments

Digital Business Methodology

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Real-World Evidence (RWE) Integration: Supplement trial data with real-world insights for drug effectiveness and safety.	Supplement trial data with real-world insights for drug effectiveness and safety.
Biomarker Identification and Validation: Validate biomarkers predicting treatment response for targeted therapies.	Utilize bioinformatics and computational biology to validate biomarkers predicting treatment response for targeted therapies.
Collaborative Clinical Research Networks: Establish networks for better patient recruitment and data sharing.	Leverage cloud-based platforms and collaborative software to establish networks for better patient recruitment and data sharing.
Master Protocols and Basket Trials: Evaluate multiple drugs in one trial for efficient drug development.	Implement electronic data capture systems and digital platforms to efficiently manage and evaluate multiple drugs or drug combinations within a single trial, enabling more streamlined drug development
Remote and Decentralized Trials: Embrace virtual trials for broader patient participation.	Embrace telemedicine, virtual monitoring, and digital health tools to conduct remote and decentralized trials, allowing patients to participate from home and reducing the need for frequent in-person visits
Patient-Centric Trials: Design trials with patient needs in mind for better recruitment and retention.	Develop patient-centric mobile apps and web portals that provide trial information, virtual support groups, and patient-reported outcome tracking to enhance patient engagement, recruitment, and retention
Regulatory Engagement and Expedited Review Pathways: Engage regulators early for faster approvals.	Utilize digital communication tools to engage regulatory agencies early in the drug development process, enabling faster feedback and exploration of expedited review pathways for accelerated approvals
Companion Diagnostics Development: Develop diagnostics for targeted recruitment and personalized treatment.	Implement bioinformatics and genomics technologies to develop companion diagnostics that can identify patient subpopulations likely to benefit from the drug, aiding in targeted recruitment and personalized treatment
Data Standardization and Interoperability: Ensure seamless data exchange among research sites.	Utilize interoperable electronic health record systems and health data standards to ensure seamless data exchange among different research sites, promoting efficient data aggregation and analysis
Use of AI and Predictive Analytics: Apply AI for drug candidate identification and data analysis.	Leverage AI algorithms and predictive analytics to analyze large datasets, identify potential drug candidates, optimize trial designs, and predict treatment outcomes, accelerating the drug development process
R&D Investments: Improve the drug or expand indications	Utilize computational modelling and simulation techniques to accelerate drug discovery and optimize drug development processes