Friendly Score presents a vision for 'social scoring' aimed at helping banks determine customer repayment likelihood and loyalty. The deck relies heavily on the team's personal backgrounds—ranging from serial entrepreneurship to amateur boxing—and early pilot data from the Polish market. While the deck identifies a clear pain point for lenders (repayment and fraud), it lacks deep technical detail on the '20+ correlations' found in Facebook data. The competitive analysis is aggressive, claiming major rivals lack a product despite significant funding. Ultimately, this is a high-level conceptual…
Key takeaways
- The company positions itself as 'The Real Big Data Social Scoring' solution for financial institutions (Slide 1).
- The core problem addressed is the uncertainty banks face regarding loan repayment and customer loyalty (Slide 2).
- CEO Maciej Dolinski highlights a track record of 4 startups with 2 successful exits (Slide 3).
- The team includes a Chief Analyst who is a PhD student specializing in NP-hard problems and a former Credit Suisse analyst (Slide 3).
- Friendly Score names Kreditech and Lenddo as main competitors, claiming they lack a product despite $27.5 million in investment (Slide 4).
- Early traction is demonstrated by a 42% opt-in rate for the score among new customers at Kasomat.pl (Slide 5).
- The product claims to have identified over 20 correlations through Facebook profile analysis (Slide 5).
- The business model includes a 'low risk' entry point for customers by offering the first analysis for free (Slide 6).
Friendly Score: The Social Media Credit Frontier
Friendly Score entered the fintech space during the height of the 'Big Data' hype cycle, specifically targeting the intersection of social media and consumer lending. The deck is a lean, high-level presentation that focuses on the team's ability to solve complex analytical problems and early proof-of-concept data from the Polish lending market. It positions social data as the missing link in traditional banking risk models.
Slide 1: Title and Positioning
The cover slide establishes the brand with a vibrant pink and purple color palette. The subtitle, "THE REAL BIG DATA SOCIAL SCORING," is a bold claim that immediately sets a competitive tone. By using the word 'Real,' the company implies that other solutions in the market are either superficial or not truly data-driven. The logo, a stylized sticky note, suggests a 'friendly' and accessible approach to what is usually a cold, bureaucratic process: credit scoring.
Slide 2: The Problem Statement
Slide 2, titled "WE SOLVE PROBLEMS," uses simple iconography to represent the banking sector. It poses two fundamental questions that lenders face: "Does my customers repay the loan?" and "Will my customer be loyal?" The slide is minimalist, using a 'Bank' icon and two 'buddy' icons (white and red). While the grammar is slightly off ("Does my customers"), the intent is clear: Friendly Score is positioning itself as a tool for both risk mitigation (repayment) and marketing/retention (loyalty).
Slide 3: The Team
The team slide is one of the more detailed slides in the deck, focusing on pedigree and personal quirks to build trust. Maciej Dolinski (CEO) is highlighted as a serial entrepreneur with "4 startups with 2 successfull exits" and experience at P&G. Interestingly, the slide includes that he is an "Amateur Boxer and Capoerista," likely intended to signal discipline and energy.
Emilian Siemsia (CTO) is described as an experienced developer specializing in natural language processing (NLP). His personal note mentions owning a "farm with 35 Acres of land," which is an unusual detail for a tech pitch but adds a layer of personal stability. Krzysztof Sornat (Chief Analyst) provides the academic weight, noted as a "PhD student specialized in solving NP-hard problems" and a former analyst at Credit Suisse . The mention that he "runs 100 km without break" reinforces the theme of high-stamina, high-performance individuals.
Slide 4: Competitive Landscape
This slide is uncharacteristically aggressive. It identifies Kreditech and Lenddo as the two main competitors. The deck claims these companies "focus only for own loan risk management" and, most provocatively, "Don’t have a product despite 27,5 mln $ investment." In the world of venture capital, claiming a well-funded competitor has no product is a high-risk strategy; it demands that the founder prove their own product is significantly further along. It frames Friendly Score as the 'lean' and 'functional' alternative to 'bloated' competitors.
Slide 5: First Results and Traction
Traction is demonstrated through a pilot with Kasomat.pl , a Polish lending platform. The key metric provided is that "42% new customers at Kasomat.pl agree for the score!" This is a crucial data point because it addresses the 'opt-in' hurdle—whether users will actually allow a third party to scan their social media for credit purposes. The slide also claims the discovery of "20+ correlations on hand of facebook profile analysis" and the implementation of "Fraud Alerts for Kasomat employees." This moves the company from a theoretical concept to a functioning B2B service.
Slide 6: Benefits for Customers
The final slide in this set outlines the value proposition for the B2B client (the bank or lender). It lists four main points:
Low risk – First analysis for FREE: A classic 'land and expand' sales strategy. · Additional $$$ – Improved payback rate: The direct financial benefit of better risk modeling. · Additional Fraud protection tool without investment: Positioning the score as a multi-use tool. · Customer Profile analysis: The ability to look for data points beyond just creditworthiness, potentially for marketing or cross-selling.
What Works in This Deck
Specific Traction Metrics: Citing the 42% opt-in rate is excellent. It answers the investor's immediate question: "Will people actually let you see their Facebook data?" By showing that nearly half of the users agreed, they prove the viability of the data collection method.
Team Credibility: The mix of entrepreneurial success (Dolinski), technical expertise (Siemsia), and high-level financial analysis (Sornat) creates a well-rounded impression. The inclusion of 'NP-hard problems' and 'Credit Suisse' provides the necessary intellectual authority for a data-science-heavy startup.
Clear Value Proposition: The deck doesn't hide behind jargon. It asks the simple questions banks care about: Will they pay me back? Will they stay? This clarity is helpful for early-stage investors who need to understand the 'why' before the 'how.'
What is Missing from This Deck
The 'How' (Technical Depth): While they mention 20+ correlations, they don't explain what those correlations are. In a space as sensitive as credit scoring, investors often want to know if the data is predictive or just noise. Is a person who likes 'Classical Music' more likely to pay a loan? Without examples, the 'Big Data' claim feels thin.
Regulatory and Privacy Considerations: Using Facebook data for credit scoring is a regulatory minefield (GDPR, FCRA, etc.). There is no mention of how they handle data privacy, consent beyond a simple opt-in, or the legalities of using social data for financial decisions.
Business Model and Ask: The provided slides do not show how the company makes money (beyond the 'first analysis for free' mention) or how much capital they are looking to raise. We also see no financial projections or market size (TAM/SAM/SOM) analysis.
Founder Takeaways
Leverage your 'weird' team facts: The inclusion of boxing, farming, and ultra-marathon running makes the founders memorable. In a sea of identical resumes, these details suggest grit and personality. However, ensure they don't overshadow the professional qualifications.
Be careful with competitor bashing: Claiming a competitor with $27.5M in funding has 'no product' is a dangerous game. If an investor knows someone at those firms, they can debunk that claim in one phone call. It is usually safer to focus on how your product differs rather than claiming the competition is non-existent.
Focus on the 'Opt-in': If your business relies on user-permissioned data, your most important metric is the conversion rate of that permission. Friendly Score was right to put the 42% figure front and center.
Frequently asked questions
- What is the primary data source for Friendly Score's credit assessment?
- Based on Slide 5, the primary data source is Facebook profile analysis. The company claims to have identified over 20 correlations between social media data and creditworthiness or fraud risk. This approach was part of a broader trend in the early 2010s to use non-traditional 'Big Data' to supplement or replace standard credit bureau scores.
- How does the company differentiate itself from established competitors?
- Friendly Score takes a direct shot at competitors Kreditech and Lenddo on Slide 4. They claim these competitors focus only on their own loan risk management rather than providing a third-party service. Most notably, they assert that these competitors 'Don’t have a product' despite having raised $27.5 million, positioning Friendly Score as a more efficient or ready-to-market alternative.
- What evidence of market validation does the deck provide?
- The deck cites 'First Results' on Slide 5, specifically mentioning a partnership with Kasomat.pl. They report that 42% of new customers at that platform agreed to be scored. Additionally, they mention providing 'Fraud Alerts' for Kasomat employees, suggesting the tool has utility beyond simple credit scoring, extending into security and identity verification.
- What are the specific benefits offered to B2B customers?
- According to Slide 6, the benefits include an 'improved payback rate' (more revenue), a 'fraud protection tool without investment,' and the ability to search for information beyond basic creditworthiness. They also use a 'freemium' hook for enterprise sales, stating that the first analysis is provided for free to lower the barrier to entry for banks.
- Is there a financial ask or valuation mentioned in the deck?
- No. The six slides provided do not include a 'The Ask' slide, a use of funds breakdown, or any mention of current valuation or previous funding rounds. While the source listing indicates the full deck has 12 slides, the provided portion focuses entirely on the problem, team, competition, and early results.
