The advent of Artificial Intelligence has resulted in disruption of everyday activities. The banking and financial industry undergoes metamorphosis courtesy of this technology. As a result, Banks and Fintech organizations see their processes change. AI’s rapid development and proliferation in the financial industry require financing organizations to adopt this technology and encourage a welcoming culture toward innovation.
In this sense, Artificial intelligence has transformed lending and the application of loans. Previously, this was a time-consuming process because of excessive documentation in the early stages. However, utilizing AI makes the process faster since credit evaluations and background checks can be conducted anonymously and quickly. Therefore, there is no need for human intervention as it reduces the overall time spent boosting efficiency.
In addition, there is more room for data-driven choices to be made. Through the predictive analysis capacity of AI, the customer experience is enhanced. AI’s impact in finance ranges from having Chabot to enhancing customer experiences and using complex risk assessment algorithms. Long gone are the days of exhausting and long paperwork. At the click of a button, Kenyan businesses can access credit, heightening financial inclusion because of easier accessibility. This article discusses “5 Ways AI Is Changing Lending For Kenyan Businesses.” It delves into real-life examples and provides an in–depth discussion of what AI in lending means for Kenyan business owners.
JP Morgan Case Study
J.P. Morgan Chase & Co. is an American bank utilizing AI to interpret commercial loan agreements that take long periods. The bank implements a COIN (Contract Intelligence) program, significantly boosting its efficiency and error reduction. It is estimated this program eliminated 360,000 hours of work annually.
This was the workload for lawyers and loan officers, which involved integration of the loan agreement. Furthermore, through ML (Machine Learning), you can review and interpret loan agreements automatically. In this sense, not only time has been saved, but the frequency of human errors is reduced. It is reported that 12,000 annual errors were prevalent previously.
COIN is an integral part of JPMorgan’s migration to automation of its services and zeal for innovation. The bank has allotted $9.6 Billion towards technology development. Previously, they invested over $1 billion in technology development. Their significant investment in technology is indicative of a strategic move to leverage the power of AI in financial services, providing a precedent for AI transformation of traditional banking processes.
5 Ways AI Is Changing Lending For Kenyan Businesses.
Financial Inclusion
AI has fostered financial inclusion in Kenya by enabling other financing solutions and micro–lending. Smaller businesses or individuals who lack access to the traditional means of credit have financing solutions. In this sense, communities are empowered to grow as people can scale up operations and do many other things with access to credit.
For example, M-Shwari was launched in 2012 by Safaricom PLC and NCBA Group. M-Shwari is a mobile banking platform that is integrated into M-Pesa (a popular mobile money Kenyan financial service). Users can save and earn interest on their deposits on M - Shwari.
Furthermore, based on transaction history and other data points that are collected with consent, creditworthiness can be assessed, and customers can have access to microloans. Over 30 million registered users are low–income individuals and small business owners. Through M-Shwari, Kenyans can access loans, save, and plan for a more secure financial future.
Regulatory Compliance
Given AI is in its developmental stages, it is expected that more advancement will occur in the near future. Therefore, AI affects financial institutions’ regulatory compliance (Fintechs, Banks, etc.) because of its rapidly evolving nature. Organizations are coming to terms with the implications of this technology on services.
Artificial intelligence is developing at an extremely rapid rate. In this sense, policy development will require frequent monitoring and evaluation. Through consistent M&E, organizations can update their policies to foster responsible and ethical use of AI in lending. Furthermore, organizations can keep an eye on developing policies they require to onboard.
Regulatory compliance protects an organization from penalties and harm. The CBK (Central Bank of Kenya) established the financial regulations in Kenya. The country’s monetary authority is responsible for maintaining financial stability, protecting consumers, and promoting economic growth.
Streamlined Loan Processing
The loan application process has become easier because of Machine Learning and AI. By relying on ML & AI, lenders can use this technology to automate document verification, reducing the time spent on applications. The heightened efficiency is an advantage to business owners and individuals who require quick access to funds. As stated by Tripathi (2024), a survey was conducted, and it was discovered that 46% of 250 participants noted loan processing takes up to five weeks.
In other situations, it can take months to go from stage to stage, experiencing delays with document verification, inadequate communication among respective parties, and unclear loan decisions becoming precedents. This situation is highly frustrating to borrowers, and they are likely to miss time-sensitive payments and pay high-interest rates easily. Eventually, they lose trust and confidence in the institution’s financial services.
Optimized Debt Recovery
Real–time data is the cause of problems in loan applications and debt payments. Debtors may have changes to initial details input during loan application. These details could be a phone number, name or address. Locating the specific file in the middle of stacked documents can be challenging. By leveraging the power of machine learning and AI, financial service institutions can automate communication, send reminders of payments, and send messages to encourage timely payment and foster a favorable relationship. Furthermore, it can be used to analyze the repayment to predict potential suggestions of loan restructuring after defaults, provide credit to businesses much faster, and ensure customers maintain a good credit standing.
Boosted Credit Scores
The traditional credit scoring methods are based on limited data points, excluding Kenyans in the informal sector. As a result, potential borrowers lack a record of financial history. However, AI–powered systems will help provide diverse data sources such as M-Pesa transactions or digital financial transactions to provide accurate and inclusive creditworthy assessments. The doors of financial services will be widened because of the inclusion of the informal sector. In addition, AI will help lenders improve their risk management practices. Credit scoring can be automated through extracts from documents and historical data. Furthermore, this historical data will help lenders evaluate the risks, potential indicators, and creditworthiness.
How Tala Leverages AI and Technology
Tala , a Kenyan mobile lending pioneer, showcases AI’s power to unlock financial access for the underserved. Here’s how they do it:
- Data-Driven Decisions: When you download the Tala app and apply for a loan, the decision isn’t based on traditional banking factors like collateral or paperwork. Instead, Tala’s AI system analyzes data from your smartphone and answers a few questions to determine your creditworthiness.
- Inclusive Lending: This approach opens up opportunities for people who lack formal financial records, a major barrier in Kenya. By looking beyond the usual metrics, Tala extends credit based on a broader picture of an individual’s financial behavior.
- Frictionless Experience: Applying and receiving a loan through your phone is incredibly convenient, especially compared to in-person interactions, which can be time-consuming and prone to bias.
- Building Credit History: Tala helps borrowers enter the formal financial system by reporting positive repayment behavior to Kenya’s Credit Reference Bureau. This builds a financial identity and opens up access to further services from banks and other lenders.
Conclusion: The Future of Lending with AI
AI’s transformative impact on lending is undeniable. It streamlines processes, enhances accuracy, and bolsters risk assessment, leading to faster approvals, better decisions, and reduced fraud. This opens doors for previously underserved borrowers, fueling financial inclusion.
Yet, challenges remain. Data privacy and security are paramount, demanding robust security measures and compliance with rigorous standards like guidelines issued by Office of the Data Protection Commissioner in Kenya, GDPR and CCPA. Additionally, we must actively address potential biases in AI systems, ensuring fairness and transparency in lending.
Despite these hurdles, AI’s benefits are clear. By responsibly harnessing its power, lenders can achieve new levels of efficiency and inclusivity. As AI continues to evolve, we can expect even more innovation. The future of lending will see smarter systems, further personalization, and expanding access to credit for those who need it most. The potential and current impacts of AI in lending are undeniable. There is a shift occurring from a traditional form of delivering financial services courtesy of AI. In this light, it is essential that AI is adopted strategically to enjoy the potential benefits and unlock its potential to change Kenyan lending.
Key Takeaways
- AI drives significant improvements in lending speed, accuracy, and risk assessment.
- It boosts financial inclusion, serving previously underbanked populations.
- Data privacy, security, and combating bias are essential for responsible AI implementation.
- AI’s continued development promises further advancements in lending.
References
Imbo, F. (2023, November 28). Unlocking the Future: The Impact of AI and Machine Learning in Financial Services. LinkedIn. https://www.linkedin.com/pulse/unlocking-future-impact-ai-machine-learning-financial-imbo-mprsk–wycbf/?trk=public_post
Jackson , K. (2023, November 29). Innovate or Stagnate: Embracing AI in Banking. Linkedin. https://www.linkedin.com/pulse/innovate-stagnate-embracing-ai-banking-kim-jackson-g8jnc/
Tripathi, P. (2024, January 17). Artificial Intelligence is Revolutionizing Bank Fraud Detection: Benefits and Limitations. DOCSUMO. https://www.docsumo.com/blog/artificial-intelligence-lending
Welsch, C. (2023, November 10). Improving Lives With AI & Financial Inclusion In Kenya. CIO Africa. https://cioafrica.co/improving-lives-with-ai-financial-inclusion-in-kenya/