The connection between an ancient Incan record-keeping system and cutting-edge artificial intelligence might seem unlikely at first glance, but it perfectly illustrates how innovation can draw inspiration from the most unexpected sources. The quipu—an intricate system of knotted cords that functioned like an abacus for the Incas—serves as the namesake for a groundbreaking Colombian financial services company that is revolutionizing how creditworthiness is assessed for microbusinesses across Latin America.
This innovative approach represents just one example of the rapidly expanding role that artificial intelligence is playing throughout the global financial services industry. As financial institutions worldwide grapple with evolving customer expectations, regulatory requirements, and competitive pressures, AI has emerged as both a transformative tool and a strategic necessity.
The Scale of AI Adoption in Financial Services
The financial services sector has distinguished itself as one of the most aggressive adopters of artificial intelligence technology across all industries. According to comprehensive research by Statista, financial institutions “exhibit one of the highest adoption rates across industries,” demonstrating a clear recognition of AI’s potential to transform traditional banking and financial services operations.
The scale of this investment is staggering. Statista estimates that in 2024 alone, the financial services industry invested approximately $45 billion in AI technology—a figure that underscores both the perceived value of these technologies and the substantial resources that institutions are willing to commit to digital transformation initiatives.
This massive investment reflects a broader industry consensus about AI’s strategic importance. NVIDIA’s comprehensive global State of AI in Financial Services: 2025 Trends report reveals that more than half of the companies surveyed view AI as “crucial to their future success.” The survey, which gathered responses from 600 financial services professionals, found that an overwhelming 98% of managers indicated their organizations plan to increase AI infrastructure spending during the current year.
These statistics paint a picture of an industry in the midst of fundamental transformation, where AI adoption has moved from experimental pilot programs to core business strategy. Financial institutions are no longer asking whether to invest in AI, but rather how quickly they can scale their implementations and what competitive advantages they can gain through more sophisticated applications.
Current AI Applications in Banking
Most banks have already begun integrating AI into their core operational processes, focusing initially on areas where automation can deliver immediate efficiency gains and cost reductions. Customer onboarding has been significantly streamlined through AI-powered identity verification and document processing systems that can complete tasks in minutes that previously required hours or days of manual review.
Credit scoring represents another area where AI has made substantial inroads, with machine learning algorithms analyzing vast datasets to assess creditworthiness more accurately and quickly than traditional methods. These systems can process thousands of data points simultaneously, identifying patterns and correlations that human underwriters might miss while reducing the time required for lending decisions.
Fraud detection has perhaps seen the most dramatic improvements through AI implementation. Modern AI systems can analyze transaction patterns in real-time, identifying suspicious activities with remarkable accuracy while minimizing false positives that frustrate legitimate customers. These systems continuously learn from new data, adapting to evolving fraud schemes and improving their detection capabilities over time.
Loan processing has also been transformed through AI automation, with systems capable of handling routine applications from initial submission through approval or denial. This automation has dramatically reduced processing times while improving consistency in decision-making and reducing operational costs.
Financial institutions increasingly view AI as essential for meeting evolving regulatory requirements, particularly in anti-money laundering (AML) and know-your-customer (KYC) compliance. AI systems can monitor transactions continuously, flagging suspicious patterns and maintaining the detailed records required by regulators while reducing the manual effort required for compliance activities.
The Evolution from AI to Generative AI
As AI capabilities continue to mature, financial institutions are beginning to explore the next frontier: generative artificial intelligence (Gen AI). Understanding the distinction between traditional AI and Gen AI is crucial for appreciating the transformative potential of these newer technologies.
Traditional artificial intelligence excels at performing tasks that previously required human cognitive abilities, relying on historical data and rules-based systems to recognize patterns, understand language, and detect anomalies—particularly the types of anomalies that indicate fraudulent activity. These systems analyze existing content and data to make decisions or predictions based on learned patterns.
Generative AI represents a specialized branch that goes beyond content analysis to actually create new content. Gen AI can write original text, simulate human conversations with remarkable sophistication, generate functional computer code, and even create images and videos. This creative capability opens entirely new possibilities for how financial institutions can interact with customers and manage their operations.
The practical difference between AI and Gen AI becomes apparent when examining chatbot functionality. A traditional AI-powered chatbot responding to the question “Why was my credit card application denied?” might return a standardized list of common reasons for credit denial followed by a customer service phone number for further assistance.
In contrast, a Gen AI-powered chatbot could provide a much more sophisticated and personalized response: “Your credit card application was denied because your credit score is too low. Your credit score is too low because a $2,000 write-off appears on your credit report. This write-off seems to be related to an auto loan from ABC Motors. Repaying this debt will help you improve your credit score. You may want to contact ABC Motors to settle this debt. Consider negotiating a ‘pay-for-delete’ arrangement.”
This enhanced capability represents just the beginning of what Gen AI can accomplish for financial institutions. These systems can analyze customer data to develop highly tailored marketing strategies and customize financial services to individual needs with unprecedented precision. They can improve loan and investment strategies by generating complex “what if” scenarios that help banks understand how changing economic conditions might affect customer behavior and institutional risk profiles.
Addressing Financial Inclusion Through Innovation
The transformative potential of AI and Gen AI extends far beyond operational efficiency improvements to address fundamental challenges in financial inclusion. This is where innovations like Quipu become particularly significant, demonstrating how advanced technology can create opportunities for underserved populations.
Bancolombia Ventures, which focuses on partnerships with startups in fintech, climate-related technology, and cybersecurity, has nurtured Quipu as part of its commitment to addressing the unique challenges of Latin American markets. The company’s approach directly tackles what Bancolombia characterizes as the “informal” nature of business operations throughout the region.
According to El País, a leading Colombian newspaper, approximately 95% of all businesses in Colombia qualify as microenterprises—defined as operations with 10 employees or fewer. Despite employing 65% of the Colombian workforce, these organizations typically suffer from what experts call “business dwarfism,” an inability to grow beyond their initial small scale. The primary obstacle to growth is limited access to capital, as traditional credit scoring methods consistently classify these businesses as high-risk borrowers.
Mercedes Bidart, Quipu’s CEO and founder, brings both academic credentials and practical understanding to this challenge. As an MIT graduate who has studied the Colombian business environment extensively, Bidart recognizes that most microentrepreneurs in the country operate essentially as freelancers rather than formal business entities.
“They have their digital wallet or bank account as a person, not as a business,” Bidart explains. “They come in for an SME (small or midsize enterprise) loan at the bank, but they won’t get that. There’s no information about their business behavior.”
Innovative Alternative Data Analysis
Quipu’s revolutionary approach involves developing entirely new methods for detecting and measuring business value that go far beyond traditional financial metrics. The company’s AI-powered system analyzes business location data, social media posts including videos, pictures, and customer comments, and numerous other nontraditional information sources to assess business health and growth potential.
Even seemingly mundane data sources like Google Maps can provide valuable insights into business development. The system can track the physical expansion of a home-based garage over time, identifying growth patterns that traditional financial analysis might miss entirely. This comprehensive approach to alternative data analysis allows Quipu to develop proprietary credit scores for microbusinesses that have been consistently overlooked by conventional lending institutions.
The practical impact of this innovation becomes evident in Quipu’s operational results. Many of the company’s potential clients are referred by Bancolombia from its pool of declined loan applicants—businesses that traditional underwriting processes have deemed too risky for conventional lending. Over the past 18 months, Quipu has extended loans ranging from $100 to $2,000 to many of these microbusinesses, representing a total of $3.5 million in loans granted to previously excluded borrowers.
While these are structured as personal loans rather than formal business loans, Bidart believes these relatively small capital infusions can help businesses grow to the point where they eventually qualify for more traditional SME loans from conventional banks. This approach creates a pathway for businesses to graduate from informal operations to formal financial relationships.
Addressing Predatory Lending
The social impact of Quipu’s work extends beyond simple financial inclusion to address serious problems with predatory lending throughout Latin America. Bidart emphasizes the harsh reality facing many microentrepreneurs who lack access to formal financial services.
“The people we serve—before us the only financial solution they had was the predatory lender. We have loan sharks. They charge abusive interest rates, and they’re violent,” Bidart explains. “They operate from Mexico to Argentina. In Colombia, loan sharks were these businesses’ only solution. We’re an alternative to that.”
This stark assessment highlights how AI-powered financial innovation can address not just economic challenges but fundamental issues of personal safety and community well-being. By providing legitimate alternatives to dangerous informal lending networks, companies like Quipu demonstrate how technology can create positive social change while building sustainable business models.
The Broader Implications
Quipu’s success illustrates several important trends shaping the future of AI in financial services. First, it demonstrates how alternative data sources can unlock opportunities for previously underserved populations when analyzed through sophisticated AI systems. Second, it shows how partnerships between established financial institutions and innovative fintech companies can create value for both organizations while serving broader social purposes.
The company’s approach also highlights the importance of understanding local market conditions and cultural contexts when developing AI-powered financial solutions. What works in developed markets with extensive formal financial infrastructure may not translate directly to markets characterized by informal business practices and limited traditional data sources.
As financial institutions worldwide continue expanding their AI investments and capabilities, the lessons learned from innovations like Quipu will become increasingly valuable. The combination of advanced technology, creative problem-solving, and deep market understanding that characterizes Quipu’s approach offers a blueprint for how AI can address complex challenges while creating sustainable business opportunities.
The evolution from traditional AI to generative AI promises to unlock even more sophisticated applications, but the fundamental principles demonstrated by Quipu—using technology to serve underserved populations while building viable business models—will remain relevant regardless of the specific technologies involved. As the financial services industry continues its AI-driven transformation, success will depend not just on technological sophistication but on the wisdom to apply these powerful tools in ways that create genuine value for all stakeholders.
Acknowledgment: This article was written with the help of AI, which also assisted in research, drafting, editing, and formatting this current version.
