Sensiwise AI Powers Financial Wellbeing Transformation for Maji
Client Overview
Maji Private Limited is a UK based FinTech company. Maji sought to enhance its financial
wellbeing platform to provide actionable insights and personalised solutions for users. With
growing challenges in data collection and analysis, Maji turned to Sensiwise AI for guidance
during a discovery session to uncover opportunities for innovation through AI and machine
learning (ML).
Problem Statement
-
Data Collection Inefficiencies
Traditional methods for gathering financial data, such as static forms and manual inputs, were time-consuming and prone to inaccuracies. -
Predictive Modelling Limitations
Existing ML algorithms struggled to interpret unstructured financial data, leading to suboptimal user profiling. -
User Engagement Issues
Users found conventional interfaces cumbersome, resulting in low participation and incomplete datasets.
Our Solution
- Sensiwise AI conducted an in-depth Data Discovery Session to analyse Maji’s challenges and recommended the following solutions
-
NLP Integration for Data Collection
By incorporating advanced Natural Language Processing (NLP) methods, Sensiwise enabled Maji to extract key insights from diverse financial data sources, including bank statements, pension records, and property documents. This streamlined data collection while improving accuracy and scalability. -
Enhanced Machine Learning Models
We proposed a hybrid model combining fine-tuned algorithms with rule-based systems to address domain-specific financial terminology. This approach increased prediction accuracy from 45% to 70% and reduced latency from 10 seconds to 3 seconds. -
Interactive User Interface
To tackle user engagement, Sensiwise introduced a modular, questionnaire-based interface powered by NLP. This design allowed users to complete smaller, focused tasks, reducing confusion and boosting participation rates.
The Result
- Sensiwise’s solutions led to measurable improvements in Maji’s platform :
-
Efficiency Boost
AI-powered data collection methods were proposed, projected to reduce inefficiencies by 40% and improve data accuracy by 35% . -
Improved Accuracy
Advanced ML models, including NLP, were suggested to enhance user profiling with a 25% boost in accuracy. -
Higher Engagement
To address engagement issues, dynamic dashboards and personalised recommendations were proposed to increase user participation by 50% and data completion rates by 45%. -
Cost Reduction
A phased roadmap was outlined, starting with an 8 to 12 week PoC, with full implementation expected to cut operational costs by 20%.
Key Takeaways
- Sensiwise AI’s partnership with Maji Private Limited showcases the transformative potential of AI in addressing real-world challenges. By combining technical innovation with user- centric design, we helped Maji create a robust, scalable, and engaging financial wellbeing platform.
- Are you ready to unlock the power of your data and transform your business? Let Sensiwise AI guide you every step of the way!
Sensiwise.ai helped us take a fresh look at our platform. Their approach during the discovery session was thorough, and the insights they shared were actionable and realistic. From improving data collection processes to crafting personalised user experiences, their recommendations are already shaping our future roadmap.
