Learn how AI applies to fintech sector with real-life examples
Artificial Intelligence (AI) is rapidly penetrating every sphere of everyday life. Smartphones, smartwatches, smart homes, automated stores and restaurants, IoT, chatbots, email filters, SIRI assistant, Google Lens, digital maps showing the shortest route based on current traffic conditions, personalized web-interfaces and ads, autopilots and self-driving cars are not mere products of sci-fi anymore. AI is all around us and we don’t even notice it since it’s deeply integrated into our daily routines.
Fintech services do not refrain from using AI either. On the contrary, they embrace innovation and take an active part in a global AI evolution.
How AI applies to fintech
Here are some examples of how AI is implemented in the fintech sector.
- Payment reminders. The majority of mobile banking apps analyze personal account information to provide prompt alerts about regular payments. Their customers should never worry they’ll miss a monthly utility bill, credit installment, or replenishment of a deposit account.
- Pre-overdraft alerts. Using a credit card sometimes leads to irresponsible spending. AI algorithms will help users track their card balance. App alerts notify the holders about their low credit balance so that they avoid overdraft activities.
- Fraud prevention. When you activate your bank account online from a new device, you are likely to become a target of a fraud alert. Unusually big money transactions or too many transactions within a short period also attract the bank’s attention. The AI algorithms help detect suspicious financial operations and notify the user by phone, email, text, or mobile banking app. In some cases, the users need to call their bank and verify the transaction in person with additional security questions.
- Price setting. Recently, the ride-sharing company Uber has reported using artificial intelligence to charge customers based on their likely payment capacity. Their new pricing system includes not only the physically objective factors such as mileage, time, and geographic demand, but also sociological variables. For instance, they take into account the average prosperity of the dwellers, of destination and location places, time of day usually preferred for business trips, etc.
- Predictive recommendations. Commercial platforms analyze a user’s habits to offer products and services to buy. The refined algorithms of Amazon or Netflix are known for their high accuracy. They predict which items or media content you are likely to be interested in. They make suggestions at the exact time you may want to purchase. Therefore, web-resources became incredibly popular.
- Virtual financial advisors. Transactional bots and digital consultants help users wisely manage their financial plans, savings, and spending. They can guide people through a variety of insurance plans too.
- Credit risk assessment. Analyzing the applicant’s credit score and potential risks is tedious and painstaking work for a human employee. So why don’t you leave this part to AI? Using AI for credit services increases the efficiency of the proposals and speeds up the process.
- Automated Claims. Making a claim for an insurance case may be long and complicated. Transactional bots guide the customer through the process, step by step, in a conversational format. They are equipped with image recognition, fraud detection, and payout prediction algorithms. Therefore, they can smoothly help clients to file their claims and the organization to authenticate and classify them. This way, they save the time of human employees needed to check information and prepare relevant documentation. They are also really cost-effective and improve the overall customer experience.
- Contract Analysis. Machine-learning models are used to digitize hard copy documents, interpret, record, and correct contracts at high speed. The blockchain-based smart contracts are also part of the solution.
- Customer retention. AI-based analysis helps to compile a list of clients who show signs of considering canceling their subscription or service usage. Technologies detect variables to the churn effect, such as the number of downloaded statements, reading account policies, unsubscription to newsletters and mailings, complaints, problematic appeals to support, etc.
- Algorithmic trading. These models facilitate short-term trade based on quick price changes. Such operations are time-sensitive, and the AI can analyze the price movements and suggest appropriate reactions much quicker than human beings. For instance, such models will be successful for trading individual stocks versus price movements in the S&P 500 index.
Fintech startups using AI
Many fintech businesses have already adopted successful AI models to prosper in their field.
Thus, Feedzai is a company that developed a strong AI platform to fight retail fraud. Their “Any Data. Any Payment” risk engine helps banks, merchants, and other commercial entities to manage both online and offline payment risks.
Juvo is the American startup that raised $40 million to help mobile users in emerging markets improve their financial standing and improve their market inclusion. Juvo app offers micro-loans to pre-paid mobile customers when they are short on data or minutes. In the developing markets, cash transactions still prevail, but Juvo loans give the dwellers of underbanked areas a financial identity and the ability to establish a good credit score over time. Juvo’s Identity Scoring technology uses AI and game mechanics to analyze subscriber and usage data in real time. This way, they can create an identity-based relationship with anonymous prepaid users.
Another example is Kabbage that provides small businesses with prospective funding options. Their AI technology platform reviews data generated by numerous business operations to automatically understand business performance. Therefore, this successful company can deliver fast, flexible funding entirely online without redundant paperwork, or a long application process.
Kasisto offers service of KAI – a conversational AI platform with intelligent virtual financial assistants and smart bots. The digital support center is smart enough to distinguish typical cases and hand off the more sensitive inquiries that need a human touch to a live agent.
Another financial crime fighter is London-based Mimiro. It has recently raised $30 million to expand its financial risk analysis platform on a global scale. AI here allows rapid verification of transactions and the identity of participants. It increases mutual trustworthiness and financial transparency.
ZestFinance brings innovation to the underwriting process. They automate the risk management by AI data analysis which works both on-premises and in the cloud. Zest Automated Machine Learning (ZAML) customers face 15% increases in approval rates due to the faster and better credit score assessments.
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