Pagaya vs Tookitaki

Neither company has publicly disclosed a valuation, making a definitive size comparison difficult. Pagaya employs 500-1000 people, while Tookitaki has 100-500 employees.

Pagaya has raised more in total funding at $600M, compared to Tookitaki's $20M — a gap of $580M.

Pagaya holds the higher Awaira Score at 70/100, compared to Tookitaki's 45/100. The Awaira Score is a composite metric factoring in valuation, funding, stage, team size, and market presence — a 25-point gap that reflects meaningful differences in scale or traction.

Pagaya was founded by Gal Krubiner in 2016. Tookitaki was founded by Abhishek Chatterjee in 2014. Visit each company's profile on Awaira for a full founder biography.

Pagaya: Pagaya operates an AI financial underwriting network that processes consumer loan applications on behalf of lenders, using machine learning models that evaluate creditworthiness across a broader set of data signals than traditional credit bureau scores, enabling lenders to approve more applicants while maintaining or improving default rates. The Tel Aviv and New York company monetises by taking a network fee on loan volume processed through its AI underwriting system, funded by institutional investors who purchase the approved loan pools.\n\nThe company went public on NASDAQ via SPAC merger, having raised over $600 million in combined public and private funding from investors including Oak HC/FT and Viola Growth. Pagaya reports processing hundreds of billions of dollars in loan applications annually across personal loans, auto loans, and mortgage products, with network partners including SoFi, Ally Financial, and US Bank embedded in its origination technology. The business model operates as an AI network sitting between lenders who originate applications and institutional investors who fund approved loans.\n\nPageya competes in the AI credit underwriting market against ZestFinance, Upstart, and traditional credit bureau scoring models from Fair Isaac. Its network model, where multiple lenders access the same AI infrastructure and their collective data improves model performance over time, creates compounding advantages compared to single-lender AI implementations. The company has navigated regulatory scrutiny around AI lending decisions and disparate impact as financial regulators increase oversight of alternative data use in credit decisions. Tookitaki: Tookitaki builds AI anti-money laundering and financial crime compliance technology for banks and financial institutions, providing the Anti-Money Laundering Suite that uses machine learning to improve transaction monitoring accuracy, reduce false positive alert rates, and enhance suspicious activity detection across financial transaction data. The Singapore company also operates the Typology Repository, a community knowledge base of money laundering typologies that informs its ML model training.\n\nThe company raised approximately $20 million in venture funding from investors including Illuminate Financial, Jungle Ventures, and SBI Investment. Tookitaki serves regulated financial institutions in Asia-Pacific and the Middle East that face increasing AML regulatory pressure and high false positive rates from traditional rule-based transaction monitoring systems that generate large volumes of alerts requiring manual review. Reducing false positive rates is the primary commercial value proposition, as financial institutions spend billions annually on compliance analyst time reviewing unproductive alerts.\n\nTookitaki competes in the AML technology market against NICE Actimize, SAS AML, and Oracle Financial Services, as well as newer AI-native AML vendors including Quantexa and Featurespace. Its community typology repository approach differentiates it by incorporating human expert knowledge about money laundering methods into its ML training process, rather than relying exclusively on historical transaction labels that may miss novel laundering patterns. The Singapore base gives Tookitaki access to the concentrated regional banking hub and the MAS regulatory framework that supports fintech innovation.

Tookitaki was founded first in 2014, giving it 2 years of additional market experience. Pagaya was founded later in 2016. In AI, even a year or two of head start can translate into significantly more training data, customer relationships, and institutional knowledge.

Pagaya has approximately 500-1000 employees, while Tookitaki has approximately 100-500. A larger team often signals higher revenue or venture backing, but in AI, smaller teams are increasingly capable of building at scale.

Yes, Pagaya and Tookitaki are direct competitors — both operate in the AI Finance space and likely target overlapping customer segments. This comparison is especially relevant for buyers evaluating both platforms.