Sensiwise AI Powers Financial Wellbeing Transformation for Maji
The Challenge :
Data Collection Inefficiencies
Traditional methods for gathering financial data, such as static forms and manual inputs, were time-consuming and prone to inaccuracies.
The
Challenge
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
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
Results
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%.
