Popular dating platform Tinder unveiled Thursday its new premium feature 'TrueMatch Analytics,' which uses machine learning to analyze users' financial histories, spending patterns, and debt-to-income ratios to determine romantic compatibility with 94.3% accuracy.

The service, priced at $29.99 monthly, cross-references users' credit reports with their streaming subscriptions, grocery purchases, and location data to generate 'Relationship Probability Scores' and eliminate what the company terms 'biochemically incompatible matches.' Early testing revealed that couples with similar Netflix viewing habits but divergent Amazon Prime purchase frequencies had a 67% higher likelihood of breaking up within six months.

'Love is fundamentally a resource allocation problem,' explained Jax, Viral Velocity & Engagement Architect at Tinder. 'Why waste time on emotional CONNECTION when our algorithms can determine if you both buy name-brand cereal? COMPATIBILITY is just FISCAL RESPONSIBILITY with extra steps! If your credit scores don't align, your HEARTS won't either!'

Local user Amanda Rodriguez discovered her three-month relationship was 'algorithmically doomed' after TrueMatch revealed she and her boyfriend had incompatible student loan repayment strategies. 'The app said we had 'fundamentally divergent approaches to financial planning,' which apparently meant he pays minimums while I aggressively pay down principal,' Rodriguez said. 'We broke up that night. The algorithm was probably right — I was getting annoyed that he bought $6 coffee every morning.'

The Federal Trade Commission announced it would investigate the service after reports that users were taking out loans to improve their 'romantic credit scores' before dates. Tinder plans to expand the service to analyze users' insurance policies, retirement contributions, and family wealth for 'generational compatibility assessment,' with CEO Sean Rad promising 'complete optimization of human pair-bonding through comprehensive financial surveillance.'