Fin Services CTOs: Try Extreme Personalization with this 4-week GenAI Engineering Blueprint

When Business asks the CTO “that” question

Picture a Monthly Review Meeting in a Fintech.

Has there been growth in the Monthly Active Users (MAUs)?? Is Customer Acquisition Cost (CAC) trending downwards? How is the Retention Rate (RR)?

The Product Managers and the CXOs are in a state of stress if these numbers are not exactly in rude health.

They go, “Our MAUs have plateaued for some quarters, and other metrics are also under stress. We are missing that personal touch that resonates with our users. Currently, we're less able to offer financial products that truly adapt to our users' unique financial situations.”

“For example, imagine if we could offer not just generic financial advice but personalized investment strategies that adapt to each user's financial goals, risk tolerance, and historical investment patterns.”

“Or a customizable insurance product that dynamically adjusts coverage and premiums based on real-time data from the user's life events, health metrics, and financial changes. These aren't just improvements; they're game-changers.”

“Or even a tailored savings account where the interest rates and saving recommendations are not one-size-fits-all but instead dynamically adjusted based on the user's spending habits, financial goals, and even lifestyle choices. It could encourage more savings by showing users exactly how altering their spending habits could directly impact their financial future.” ?

“Each user gets a unique experience, feeling like our platform truly caters to their individual needs. And I think that would help push our metrics up!”

Then it happens! They turn towards the CTO and ask “that” question:

“All this talk of GenAI - what could you do to help us?”?

CTOs who know the ‘how’!

The CTOs are, of course, absolutely expecting these questions these days.

“Guys, your vision for hyper-personalization can be delivered with Generative AI (GenAI) tech. We can get GenAI to analyze tons of data to provide customized financial solutions, enhance user engagement, and potentially boost our metrics”, says our CTO.

She continues, “My team can do this – nothing big and shiny – give me 4 weeks and I can show what is possible. I will need ￡40-100K and we can show you initial results within 6-8 weeks post-implementation.”

Here's what she came up with (with a little support from our CTO team).

Goal: Leverage Generative AI (GenAI) to enhance Monthly Active Users (MAUs) and Average Revenue Per User (ARPU).

What could we expect?

• a personalized model that could increase customer conversion rates by 8-15%, reduce churn by 20-30%, and grow ARPU by 12-18%
• based on current baselines, expect to increase MAUs by 10% and retention rate by 25% over 6 months, post-launch

Investment Budget:

We will clearly focus on balancing performance with computational costs to ensure that the project remains financially viable, with ROI anticipated through improving business metrics over time. We will need ￡40K to ￡100k – a breakup follows in Annex 1 with the rationale.

4-Week Plan: Shorter Version

(Had to keep it short for LinkedIn. Just DM me and I will send across the full version that runs to 8+ pages)

Week 1: Foundational        

• We will focus on Retrieval Augmented Generation (RAG) for dynamic, data-driven personalization, and an auto-regressive transformer architecture like GPT-4 for next-token prediction and language generation.
• Additionally, we will incorporate non-causal bidirectional transformer layers akin to BERT to absorb larger context and high-dimensional data. RL training to ensure we directly optimize sequences of financial recommendations for long-term returns, for ex.
• The resultant model would surpass relying solely on GPT-4 models by specializing to the fintech domain with custom techniques for personalization and optimize risk-adjusted performance. Technical KPIs model inference time, accuracy, and computational costs

Week 2: Data Strategy and Model Selection        

• We will implement advanced preprocessing, utilizing differential privacy and federated learning for data anonymization without compromising utility.
• Team to source transaction graphs to model interconnected financial behaviour vs simplistic attribute/event data
• Next to think about generating privacy-preserved synthetic data mirroring distributions in actual user data
• We would then implement active learning for users to directly improve their models through feedback loops
• On Model Selection, the team actions would be to tailor RAG models with financial domain data for insightful advice, use TensorFlow Decision Forests for adaptive predictive analytics, ensuring cost-efficiency in training and runtime, establish concept drift triggers based on deteriorating model inference-time performance, maintain human-in-the-loop oversight through mechanisms like LIME model explanations, etc.

Week 3: Integration and Prototype Testing        

Integration: Ensure API adaptability with existing systems; utilize AWS Lambda for scalable deployments, focusing on secure, compliant data pipelines.

Testing: Develop a prototype; conduct A/B testing. Use TensorFlow Model Analysis for in-depth performance insights across user segments. Expose model to 5% of users initially for quantified analysis, study changes to platform KPIs and individualized business metrics versus the holdout group. Only proceed to further 20% rollout after predetermined targets are hit

Week 4: Iteration, Deployment, and Scaling        

Iterative Improvement: Refine models based on prototype feedback, optimizing for real-time performance and cost-efficiency.

Deployment Strategy: Implement deployment to minimize user disruption. Apply automated scaling and continuous monitoring to support growth. ?????? Allocate 2-3 weeks for intensive small-scale testing versus 1 week in MVP, and carefully segment users into homogeneous batches based on key attributes to isolate issues.

Cost Management and Monitoring: Use cloud cost optimization tools to maintain budget control. Establish metrics for ongoing cost-performance evaluation.

Annex 1 - This provides cost break up and justifications as to how the number was arrived at.

Covered here at high level only, but available in the longer version on request.

Here is the broad break-up:

• Cloud Costs: AWS Lambda for model deployment and inference, moderate level of API calls and computational requirements, ￡500 to ￡3K monthly.
• Development Team**: 4-week sprint with 4-5 members, costs ￡30K to ￡50K
• Software and Tools Licensing: The usual, an additional ￡1K-5K
• Data Acquisition and Processing: Depends, if using preprocessing large datasets, data complexity and volume, then higher end of ￡1K to ￡10k range
• Security and Compliance: Ensuring GDPR/ CCPA compliance, ￡5K to ￡15K ?
• Testing and Deployment: A/B tools, prototype dev & deploy from ￡2K-￡10K.

**Development Team (more specs in the longer document available on request)        

1. Data Scientists (2-3 individuals):?Expertise in GenAI technologies (e.g., RAG, GPT-4), machine learning, deep learning, and data analytics. Familiarity with TensorFlow, PyTorch, and other ML frameworks. They will do Model development, training, and fine-tuning. Data analysis and insights generation.
2. ML Engineers (2 individuals): Proficiency in implementing, scaling, and deploying AI models in production environments. Experience with AWS Lambda, Docker, and Kubernetes for deployment. They will be operationalizing models, ensuring scalability and efficiency. Integration with existing systems.
3. Data Engineers (1-2 individuals): Strong background in data architecture, ETL processes, and handling big data technologies (e.g., Hadoop, Spark). Knowledge of data privacy practices. They will do data preprocessing, cleansing, and ensuring data quality. Implementing data privacy and security measures.
4. Software Developers (2 individuals): Experience in backend development, API integrations, and frontend development for prototype interfaces. Familiar with agile development practices. They will build the user interface for the prototype, integrating AI outputs into user-facing products.
5. Project Manager (1 individual): Strong leadership, communication, and project management skills. Experience in tech projects, particularly AI or fintech. They will be overseeing project timelines, budget management, coordinating between teams, and stakeholder communication.
6. Security and Compliance Officer (1 individual): Knowledge of data protection laws (GDPR, CCPA), cybersecurity practices, and fintech regulations. The job would be ensuring the project meets all legal and compliance requirements.

Caution bits:        

While the regulatory environment in the UK does support innovation and the personalization of financial products, such as those mentioned above, fintechs must navigate these regulations carefully. They must ensure complying with the FCA's rules and principles, particularly the new Consumer Duty, to provide products and services that are not only innovative but also fair, transparent, and in the best interests of their customers.

Inspirations        

1. EY Article from David Kadio-Morokro and Vidhya Sekhar

2. CB Insights

3. Computer Weekly