Digital transformation is having a significant impact on the financial sector. Fintech applications allow automating complex processes and decision making with the highest level of accuracy. AI applications in fintech include:
- Smart credit scoring
- Lending software
- Natural language processing solutions for customer support and portfolio optimization
- Fraud detection
- AI in fintech data classification
Manual repetitive tasks are increasingly being automatized by AI software, leaving more time for the employees to concentrate on more creative and valuable assignments. AI augments the workforce with smart analytics, accurate predictions, and personalized customer service. As a result, financial service companies enhance operating models, upend competitive dynamics, ensure efficient compliance, and drive enterprise growth.
Let’s take a look at both general use cases and business cases from our experience as AI developers.
AI in FinTech: Business Case
AI Technologies and Their Use in Fintech
AI technology includes tools that are used in different areas and perform different functions. Among them are Machine Learning, Deep Learning, Video Analytics, Natural Language Processing, Computer Vision, and Augmented Analytics.
Depending on the purpose, environment and technology stack, one or the other technology may be applied in the financial sector. For instance, computer vision can be used to provide clients biometric security and/or data classification for employees. Natural Language Processing (NLP) offers virtual assistants and chatbots for both customers and workforce. Below is a brief overview of technologies and their applications in finance.
According to Autonomous Research, by 2030, AI technologies will allow financial institutions to reduce their operational costs by 22%.
Applications of AI in FinTech
According to Forrester, around 50% of global financial services and insurance companies already use AI, which is expected to grow with technology advancements.
AI Data Classification
In such segments as banking, insurance, and capital markets, the documentary is huge and with documents and reports not necessarily structured. To classify them manually is a time-consuming and error-prone task.
Learn more about how to classify financial data.
AI with Machine Learning capabilities can analyze the content of a given document and classify it in accordance with provided parameters. Documents can be cataloged regardless of the data type or format. As a result, the document flow is automated, releasing employees from routine tasks.
Softengi Business Case: AI-powered Data Classification
Softengi developed a transaction categorization solution that simplifies credit scoring. The solution allows agents to analyze transactions easily to create a credit scoring profile. The categories included groceries, fuel, healthcare, dining, automotive expenses, and others.
The ML-powered system analyzes available transaction information derived from sales receipts, packing slips and purchase confirmations. For example, if transaction documentation contained the name “Shell”, then AI recognized a payment for car refueling and marked the transaction as belonging to the appropriate section. Such tracking of users’ spendings allowed the software to enhance the personalized finance management system and increased clients’ engagement.
Automated Customer Support
Applying AI and allying applications allows financial services companies to provide their customers with an efficient, engaging, and personalized environment. Customer-focused AI systems, such as AI financial assistants and chatbots, provide users with practical advice on managing their finances or giving efficient recommendations related to existing investment opportunities.
Moreover, AI can automate back-office operations. For instance, AI software can automatically extract meaningful information from unstructured data sources, such as invoices, order forms, and/or payments, providing employees with greater visibility of companies’ workflow and operational performance. As a result, finance managers get access to real-time accrual reports, allowing them to more efficiently monitor and control the cash flow of their clients.
Fraud Detection for Credit Card Transactions
With the ability of AI to process huge amounts of data, financial services companies can detect fraudulent activities and prevent breaches before they occur. By detecting patterns in credit card transactions, ML-powered software promptly identifies anomalies, thus saving financial services organizations millions of dollars.
AI Credit Scoring
Due to ML capabilities and available historical data, AI can significantly enhance traditional credit scoring that is mostly focused on a cursory analysis of credit histories of potential customers. However, conventional credit scoring is neither efficient nor error-prone, as it rejects applications from debtors able to repay their loans. AI can assess the creditworthiness of potential lenders more efficiently, ensuring that customers with high credit ranking receive the best offers.
Moreover, AI can use alternative data, such as social media or asset ownership or employment data, that allow creditors to more accurately assess the financial situation of potential loan recipients. AI-powered tools are especially useful for assessing customers with limited credit history.
Advanced Analytics for Clients
Financial services companies can offer their customers AI Analytics apps that can monitor cash flow and make forecasts for financial departments. With Augmented Analytics based on AI technology, the application is able to estimate the amount of money that will move in and out of business within a short-term period, for instance next week or next month. AI analytics software can notify if a company needs to cut overheads, look for new investment, or focus on generating sales.
Learn more about Augmented Analytics.
Traders, brokers, and investment funds are increasingly applying AI to their trading strategies. The reason behind this is that AI offers a data-driven view of the current situation, makes accurate forecasts and can trade with small spreads and at high speed. Moreover, by constantly collecting and analyzing various data from news, social networks, specific website, AI can perform an in-depth analysis of current events and trends, as well as build behavioral models during business turmoil.
AI Business Process Automation
Today, most consumers and banking organizations are familiar with AI intelligent automation in financial services.
According to Capgemini, the financial services industry is expected to add around $512 billion in global revenues by implementing AI automation solutions.
Due to its ML and DL capabilities, AI is able to produce intended answers or actions that serve to improve the speed and accuracy of internal processes. For instance, JP Morgan with its ML system Coin has saved 360,000 hours of lawyers’ and loan officers’ work by autonomously reviewing and interpreting commercial loan agreements.
Business Case: Document Management Software for Bank of Georgia.
Softengi Business Case
Softengi has developed a smart parser at the request of a financial services company. The NLP-based application recognizes documents and contracts, analyzes their content, and generates only relevant information. Specifically, this software was used to analyze a huge number of documents provided for obtaining a loan, such as a passport, a financial statement, a cash flow projection, property documents, etc. The software allowed the company to automate the processing of documents, and hence improve its productivity.
Tips for Financial Services Companies that Apply Digital Transformative Initiative
Reduced paperwork, better customer support and engagement, and improved analytics are just some of AI’s benefits for financial companies. But how to implement AI into established business processes?
Here are some essential tips.
1. Apply AI in FinTech strategic plans
Deterioration of traditional profit strategies, volatile competitor landscapes, technological innovations and changing customer expectations call for a new perspective on the financial services business. Thus, we recommend that financial services companies explore AI technologies and their applications in Finance and prioritize those that meet their challenges and objectives.
2. Consider AI for operational processes and customer engagement
The two major areas where AI can bring value are operations and customer service. By implementing AI technologies, financial services can gain four result types: operations discovery, customer discovery, operations efficiency, and customer efficiency. Operations and customer discovery refer to insights uncovered with ML-powered tools that allow companies to identify costs drivers, market trends or novel criteria for enhanced customer segmentation.
On the other hand, AI allows to improve the efficiency of both operations and customer services and, as a result, enhances speed and accuracy of internal processes and improves customer conversion and loyalty.
3. Start small
It is not necessary to start implementing multiple complex applications at once. Start small and then scale up. Starting step-by-step will allow to release an application faster and then extend its applicability over time. AI applications can be iteratively scaled up, so financial services companies can expand their capabilities and reap increasing benefits over time.
4. Entrust AI software development to professionals
Despite the fact that an increasing number of startups and newly emerging software service companies offer numerous AI solutions for fintech, it is quite a challenge to find an appropriate vendor.
Creating an efficient AI solution that meets the customer’s needs and desires requires a proper tech stack, professional talent, and a great business experience, avoiding pitfalls and tailoring the AI application to companies’ objectives. By applying the most advanced AI development practices and tools, Softengi, with more than 20-year experience in the IT field, can help you harness the power of AI for answering your most complex challenges.
Contact us to discuss the implementation of AI into your workflow.