- Credit Risk Evaluation
John claims that A.I. can be used to analyse credit risk, automating a large chunk of the credit lending underwriting process, allowing lenders to make loans more quickly, and even expanding services to other consumer groups without having to hire more staff. He described how a Credit Decision Engine is created by utilising data in order to provide risk assessments for clients based on various data factors. With these techniques, loans can be approved without a trip to a bank branch or any other access point. While also managing risks to acceptable levels and offering credit lending limitations suited for the customer’s situation, he said.
- Detection of fraud
A.I. has become a crucial instrument for fighting fraud and financial crime as the number of digital channels available to operate enterprises has grown significantly over time. According to John, artificial intelligence (AI) has the natural capacity to analyse vast volumes of data and identify fraud tendencies, which may then be applied to detect fraud in real time. He clarified that this is particularly helpful in identifying fraudulent bank transactions and spotting transactions that don’t suit a particular customer’s typical behaviour. According to John, “These systems can also learn for themselves, identifying transactions that follow patterns of previously discovered fraudulent acts.” He continued by saying that artificial intelligence (AI) is essential for preventing identity fraud since it enables digital channels to confirm identity using biometrics, voice matching, and even face matching to government-issued IDs.
John pointed out that A.I. is providing marketers with new levels of understanding and long-lasting commercial benefit, particularly at a time when connecting with customers is simpler thanks to a variety of channels for communication and client acquisition. Assuring optimal efficiency, he claimed that “A.I. analyses consumer data and profiles to learn how best to engage with clients, then serves them personalised messages at the proper moment without interference from marketing team employees.” He continued by saying that corporations might utilise A.I. to prioritise leads, which is useful when they are dealing with a high volume of leads each day yet have limited resources. A.I. may prioritise leads based on their likelihood of conversion using data like drop-off information collected during client onboarding, ensuring optimal use of limited marketing resources while maximising conversion rates.
Four. Chatbot (Conversational Agents or Dialog Systems)
The rising desire for quick replies from customers is causing a new wave of change in how consumers interact with services. John claims that chatbots powered by AI use NLP to comprehend conversations and their circumstances at such a high degree that suitable responses may be generated. With the introduction of voice-controlled virtual assistants by companies like Apple (Siri), Google, and Amazon (Alexa), he explained that recent technological advancements have gone beyond text-based chats. These assistants are capable of proficiently responding to an infinite number of questions in natural language dialogue. “Voice-controlled goods and services will revolutionise how a consumer interacts with technology platforms, decreasing entry barriers and improving technology adoption,” he added. “In a market like Africa where literacy rate lags substantially behind the world’s average.”
John concluded by outlining how A.I. for operations combines complex techniques from deep learning, data-stream processing, and domain expertise to analyse data gathered from operational pipelines in order to improve efficiency, lower costs, and maintain the best possible service delivery and quality. This covers a number of verticals, depending on the sort of business, including but not limited to:
when buying products or services, estimating pricing based on historical data.
forecasting value and any impending appreciation or depreciation using macroeconomic considerations.
Predict peak times or areas with strong demand far enough in advance to plan accordingly and maximise earnings.
Management of liquidity, which involves predicting future instances of liquidity problems based on anticipated or projected growth.