By Emma Lockhart
We meet in transit. Wrocław Główny, car 12. Amid the noise of the railway station, the smell of takeaway coffee, and loudspeaker announcements, our conversation begins with a pause. This interview wasn’t meant to take place in a studio — and that was the point.
“I no longer live by a corporate schedule,” says Elias Carter shortly after he sits down across from me. He’s wearing blue geometric glasses. He smells like green tea and microprocessors.
Once the lead machine learning architect at Tamga, Carter is now an independent researcher in algorithmic ethics, a digital rights consultant, and a vocal critic of corporate AI maximalism. We talk about the past and future of fintech — and about the people he’s still trying to help through technology.
Journalist: Elias, you spent nearly six years inside Tamga. Now you’re on the outside. What exactly were you doing there — and why did it matter?
Elias Carter: In short — we were building an exoskeleton for the financial sector. Not for banks — for people. Tamga wasn’t just another fintech startup. It was an engineering lab for justice. Imagine being denied a loan just because you don’t own property in a capital city or don’t have a traditional full-time job. We operated from a different philosophy: what if it’s more important that you reliably pay for parking every month than where you were born?
Journalist: You’re talking about those 1,500 “nontraditional” variables?
Elias: Exactly. We trained our system to think differently. It wasn’t just about credit history. It included educational paths, behavioral patterns, whether you have a driver’s license, or even how often you update your operating system. We were building a digital portrait that reflected reliability as a behavioral constant — not just financial solvency.
Journalist: So it wasn’t just about making loans more accessible?
Elias: No. It was about justice. Tamga became something like Google Translate for the banking system. We translated sentences like “I always pay my debts, but the system won’t let me in” into a language the algorithm could understand. Without that translation, many people simply had no chance.
When a neural network learns empathy
Journalist: Tell me about Scorector. Is it really like GPS for your credit life?
Elias: That’s one way to put it. Scorector is a kind of credit therapist. It doesn’t just assess — it educates. “Here’s where you slipped, here’s how to fix it.” We built an algorithm that evolved from observer to mentor. It can tell you: “Pay off this debt first, wait 60 days before taking a new loan — and your score will improve by 15 points.”
Journalist: And it actually works?
Elias: More than you’d think. Over 10,000 users have already improved their credit scores. For some, that meant their first-ever decent loan, their first business, their first mortgage. These aren’t just numbers. It’s a real, living economy of small victories.
Szybka Gotówka and the phenomenon of lightning-fast lending
Journalist: What about Szybka Gotówka? That’s more than just a lending platform, right?
Elias: It’s like Tesla on steroids — but for credit. Fifteen minutes from application to payout. But the magic isn’t in the speed. It’s in the understanding. The model runs on 1,500 behavioral and social variables. We’re not guessing who will repay — we know. And we don’t exclude people based on stereotypes.
A freelancer, a taxi driver, a blogger — a traditional bank says “too unstable.” Our model says: “They’ve paid rent on time for 36 months, work with verified clients, and their cash flow is steady. They’re reliable.” This isn’t an alternative. It’s a new moral architecture for finance.
Journalist: But all of this rests on machine decisions. What if they’re wrong?
Elias: And humans don’t make mistakes?
Szybka Gotówka is a system that learns. It has analyzed hundreds of thousands of repayment scenarios.
The result?
- Over 1 million loans issued
- 85% fully automated
- Zero paperwork
- And most importantly: no discrimination based on origin, address, or employment type
An ecosystem of trust
Journalist: But how did you make money from this?
Elias: Tamga didn’t sell loans. We sold fairness infrastructure. Our clients — banks, investment funds, microfinance institutions — paid for access to accurate scoring models. We charged fees on disbursed amounts and license royalties for platform usage.
It wasn’t just a fintech company. It was an institution of trust. Algorithm + ethics + transparency = growth. Everyone won.
Journalist: So, what’s next?
Elias (pauses): More regulation, more data politicization, more demand for transparency. And that’s a good thing. Justice needs both precision and context. I believe the next phase for Tamga — or its ideological successors — is real-time personalized financial navigation.
Imagine your AI assistant saying:
- “Don’t take a loan now — interest rates will drop in three months.”
- “This mortgage isn’t favorable. Wait — a better bank offer is coming.”
- “You can safely borrow if you cut back on subscriptions.”
That’s the intuitive financial interface we should all have. That’s the future we should build.
Living unplugged
Journalist: And what are you doing now?
Elias: Like a disconnected cable from a data center. First — silence. Then — fresh air. I’m reading Umberto Eco and Herbert Marcuse. Growing rosemary. Advising projects that aren’t building yet another AI slot machine but aim to shift the paradigm. The most dangerous illness in fintech is the illusion that scalability equals meaning. I stand for a future where every user is treated not as a data point, but as a person.
Advice to newcomers
Journalist: What advice would you give to those just entering fintech?
Elias: Don’t confuse data with truth. Learn Python, yes — but also study the history of social exclusion. Look beyond APIs — look into the faces of those your models might exclude. Fair fintech isn’t just about algorithms. It’s about building a world where technology amplifies justice instead of replacing it.
Station: Lublin.
Elias Carter stands up, nods lightly, and walks away. On the table, he leaves a business card. It reads only one phrase:
“Justice can be calculated — but only if you choose to want it.”
Epilogue: The Numbers behind the story
As Elias disappears — both literally and metaphorically — I’m left alone in the compartment. But his words linger, like background processes running silently. This wasn’t a typical news piece: there’s no “new feature,” “investment round,” or “market leadership.” This was something else — a story about rethinking the ethics of finance.
I open my laptop and start digging through the numbers. Tamga wasn’t just code and models. It was:
- Over 1,500 behavioral variables that redefined credit risk
- 1,000,000+ loans issued, 85% of which were fully automated
- More than 10,000 users trained by Scorector to understand credit discipline
- 15 minutes from loan application to money in the account
It wasn’t just about finance. It was about dignity, trust, and the possibility of fairness — coded into the future.
About TAMGA
TAMGA is an international fintech company that develops and delivers technological and marketing solutions for the financial sector.
The company’s mission is to improve financial literacy and make financial products simple and accessible to everyone.
TAMGA promotes the principles of responsible lending. Its consumer credit terms are personalized, helping borrowers avoid over-indebtedness.
The TAMGA lending platform enables banks and financial institutions to tailor their loan offerings in line with internal policies and business needs.
By leveraging rich behavioral data, the platform achieves higher approval rates and lower interest rates while maintaining the same level of credit risk.
TAMGA’s product portfolio includes platforms for automating offline and online lending processes, online user verification and financial profiling services, credit scoring and improvement systems, installment payment solutions, and credit comparison websites.
