Predictive AI for FinTech Customer Acquisition
Predictive AI for FinTech Customer Acquisition
Reduce CAC and acquire more approved customers with real-time predictive signals for Meta & Google
Industry Challenges
1. High cost per lead with low approval rates
FinTech companies often pay premium prices for traffic, but a large share of leads never pass internal approval processes (KYC, credit scoring, fraud checks). As a result, marketing teams optimize for volume, while business outcomes depend on a much smaller subset of approved customers.
2. Long and complex conversion cycles (KYC, underwriting)
Unlike eCommerce, conversions in FinTech don’t happen instantly. Users go through multiple steps — application submission, identity verification, risk assessment, and approval — which may take days or even weeks. This delay breaks the feedback loop for advertising platforms, making it harder for them to learn and optimize effectively.
3. Many leads never become paying customers
A significant portion of users who submit applications either: drop off during onboarding fail verification are rejected by risk models This creates a gap between lead generation metrics and actual revenue, making traditional campaign optimization misleading.
4. Ad platforms optimize for leads, not approved users
Meta and Google optimize based on the signals they receive. If the only signal is “lead submitted,” algorithms will find users who submit forms — not users who get approved or generate revenue. This leads to: inflated lead volume declining lead quality rising CAC
5. Poor signal quality for machine learning optimization
Because high-quality outcomes (approved users, funded accounts) are relatively rare and delayed, ad platforms receive weak and sparse signals. This slows down learning and reduces the effectiveness of automated bidding strategies like Smart Bidding or Meta optimization.
6. Disconnection between marketing and risk/approval models
Marketing teams drive traffic based on surface-level metrics (clicks, leads), while internal systems (credit scoring, fraud detection) determine real business value. These systems are rarely connected, meaning: marketing doesn’t know which users are valuable ad platforms can’t optimize for real outcomes
How Tomi.ai Works
Tomi analyzes real-time behavioral data from your website to predict which users are most likely to become approved customers — not just leads. It then sends predictive conversion signals with assigned value into Meta and Google, allowing ad algorithms to optimize for high-quality users instead of raw volume.
1
Pixel Installation
15 min
2
Data Collectiion
2-6 weeks
3
ML Model Training
1-2 weeks
Depending on the sales cycle & how quickly we can catch 100 positive outcomes
How Tomi.ai Solves FinTech User Acquisition Challenges
Identifies high-value users before they convert
Instead of waiting for approvals or completed KYC, Tomi analyzes real-time behavioral signals on your website: engagement depth, navigation patterns, interaction with key pages device, geo, traffic source.
Converts behavior into predictive revenue signals
Each user session is transformed into a predictive conversion signal with assigned value. This means: not all leads are equal, each interaction gets a dynamic value based on predicted business outcome.
Sends enriched signals directly into Meta & Google
Tomi sends predictive conversion events via server-side integrations (Conversions API / Google Ads API), including: probability score, predicted conversion value, enriched event data. Ad platforms don’t just see “a lead” anymore — they see how valuable that user is likely to be.
Re-trains ad algorithms to optimize for approved users
It is hard to close the feedback loop with Ad Platforms when the actual purchase is days, weeks or even months away from website visit. Our platform solves that.
Accelerates learning with signal multiplication
FinTech often suffers from sparse conversion signals (few approvals, long delays). Tomi solves this by: generating predictive events early in the funnel, increasing signal density for algorithms.
Bridges marketing and risk/approval models
Tomi effectively connects: marketing data (traffic, behavior), business outcomes (approvals, revenue). This means: ad platforms learn what “good users”, actually look like, marketing aligns with real unit economics.
Our Clients in The FinTech Industry
May 25, 2023
Evan H.
Digital Marketing Director
Mid-Market (51-1000 emp)
Insurance
Digital Marketing Director
Mid-Market (51-1000 emp)
BEFORE
Their sales funnel in Meta looked as follows:
→ ad impressions
→ lead generation via Meta Leads
→ a call from the sales department
→ an in-person visit to the sales
office (2–3 weeks after the call)
→ apartment purchase (3–6 months later)
OUR WORK
We trained the ML model so that it would adjust advertising algorithms to find leads with a check of $100,000 and a high 90% probability of purchase.
AFTER
A/B test showed:
- 31% decrease in the cost per lead submitted on a website (cost per contact Tomi)
- 7% decrease in Meta CPL
- 36% CTR growth
+28%
sales
+35%
ROAS for 4 weeks
What do you like best?
The team at Tomi takes full ownership of their work. The onboarding was easy and described (one of the few times a project timeline was kept!). This team knows what they are doing and has been an excellent resource for us – both from a human resource perspective but also as a tool in our digital marketing program.
May 25, 2023
Evan H.
Digital Marketing Director
Mid-Market (51-1000 emp)
Real Estate
Digital Marketing Director
Mid-Market (51-1000 emp)
BEFORE
Their sales funnel in Meta looked as follows:
→ ad impressions
→ lead generation via Meta Leads
→ a call from the sales department
→ an in-person visit to the sales
office (2–3 weeks after the call)
→ apartment purchase (3–6 months later)
OUR WORK
We trained the ML model so that it would adjust advertising algorithms to find leads with a check of $100,000 and a high 90% probability of purchase.
AFTER
A/B test showed:
- 31% decrease in the cost per lead submitted on a website (cost per contact Tomi)
- 7% decrease in Meta CPL
- 36% CTR growth
$100,000+
av. order check
-31%
website CPL
+36%
CTR
What do you like best?
The team at Tomi takes full ownership of their work. The onboarding was easy and described (one of the few times a project timeline was kept!). This team knows what they are doing and has been an excellent resource for us – both from a human resource perspective but also as a tool in our digital marketing program.
May 25, 2023
Evan H.
Digital Marketing Director
Mid-Market (51-1000 emp)
Real Estate
Digital Marketing Director
Mid-Market (51-1000 emp)
BEFORE
Their sales funnel in Meta looked as follows:
→ ad impressions
→ lead generation via Meta Leads
→ a call from the sales department
→ an in-person visit to the sales
office (2–3 weeks after the call)
→ apartment purchase (3–6 months later)
OUR WORK
We trained the ML model so that it would adjust advertising algorithms to find leads with a check of $100,000 and a high 90% probability of purchase.
AFTER
A/B test showed:
- 31% decrease in the cost per lead submitted on a website (cost per contact Tomi)
- 7% decrease in Meta CPL
- 36% CTR growth
$100,000+
av. order check
-31%
website CPL
+36%
CTR
What do you like best?
The team at Tomi takes full ownership of their work. The onboarding was easy and described (one of the few times a project timeline was kept!). This team knows what they are doing and has been an excellent resource for us – both from a human resource perspective but also as a tool in our digital marketing program.
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