Finance
Transforming Financial Forecasting and Customer Retention for a Leading Bank
Focus Areas
Advanced Analytics
Customer Relationship Management (CRM)
Machine Learning (ML)
Business Problem
A regional bank with a growing customer base was facing substantial difficulties with financial forecasting accuracy and customer retention. It faced intense competition from the industry, which caused it to be under pressure to improve its financial performance. Internally, the mid-size bank faced a twofold problem – its internal teams lacked resources, and the existing infrastructure and tools were not able to bring together different data sources in the bank’s systems (managed with a patchwork of vendors handling different aspects of their technology stack).
The Approach
Through close collaboration with the bank’s internal teams, existing vendors, and key stakeholders, Curate’s consultants implemented advanced technologies and processes that could seamlessly integrate across platforms and departments. The aim was to ensure short-term improvements as well as long-term scalability.
Key components of the solution:
- Phase 1: Assessing the business and technology
The Curate team carried out in-depth discussions with the bank’s finance, customer engagement, and IT teams, a comprehensive review of the bank’s existing processes, platforms, and data infrastructure, and a detailed technical audit of the bank’s current systems involving the CRM platform, legacy data warehouses, and financial reporting tools.
Through this, Curate’s consultants identified inefficiencies and pain points including understanding the current data silos and how data could be better used to improve forecasting and customer retention strategies.
- Phase 2: Designing the solution and planning
By utilizing its deep domain expertise, the Curate team designed a tailored solution that would integrate advanced analytics and ML algorithms to analyze, in real-time, extensive amounts of customer data and financial information. This would make the forecasting process more dynamic and precise.
Concurrently, the bank’s customer data was fed into predictive models by working with existing vendors and platforms such as the bank’s CRM provider and cloud infrastructure service. This allowed the marketing team to uncover behavioral patterns to craft more personalized services without the need for a costly overhaul.
To ensure that the bank’s operations remained fully compliant with financial regulations through the transformation, the planning phase also involved developing a data governance model.
- Phase 3: Implementation:
- During this phase, our IT experts worked closely with the client’s internal IT team to deploy the analytics and forecasting models, which involved:
Integrating data from varied customer touchpoints such as CRM systems, mobile banking applications, and in-brach services.
Establishing predictive models that would improve financial forecasting by up to 15%, based on historical and real-time data.
Boosting the CRM’s custom segmentation features to allow the marketing team to share personalized service offerings with different customer segments which improved retention.
- The Curate team conducted workshops and training sessions simultaneously for the bank’s internal teams to ensure that they had the skills and knowledge required to manage and scale the new solutions. The training provided reduced the need for additional vendor support, giving the bank more autonomy over managing its analytics needs going ahead.
- During this phase, our IT experts worked closely with the client’s internal IT team to deploy the analytics and forecasting models, which involved:
- Phase 4: Optimizing and providing support
- After implementing the solution, the Curate team provided ongoing support such as fine-tuning the ML models to ensure continuous accuracy and relevancy. Reviews were conducted periodically and the required refinements were made after assessing the solution’s effectiveness. The team also remained on standby to provide help and support for future integrations, including the new customer engagement tools that the bank was considering.
Business Outcomes
Enhanced Financial Forecasting
Real time data and predictive analytics integration improved the accuracy of the bank’s financial forecasts and helped align decisions with actual market conditions.
Boost in Revenue Growth
Improved forecasting accuracy and customer retention enhanced strategic decision-making and boosted revenue growth.
Customer Value
Optimized Customer Retention Strategies
The bank was able to utilize customer data and deliver more personalized services. This improved customer loyalty and retention rates.
Improved Customer Experience
Better customer segmentation allowed the bank to offer more personalized services. This improved the overall customer experience and created the foundation for longer relationships with customers.
Conclusion
Curate successfully improved financial forecasting and optimized customer retention strategies by collaborating with the bank’s internal teams and external vendors. The utilization of advanced analytics, ML, and data integration solutions helped offer more personalized services and accurate forecasting. This allowed the bank to grow its revenue and drive significant business improvements while laying the groundwork for future innovation.
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