How the Fintech startup uses ML to disburse loans and detect fraud?
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The rise of fintech has not only enabled the banks and organizations to offer better services but has also changed the way financial operations were undertaken. Many benefits can assure you of the importance of Fintech, artificial intelligence, and machine learning in fintech are already leveraged to provide robust financing solutions. But the fintech startups have gone ahead a step now. They use these latest technology trends like AI and ML to provide quick and hassle-free credit financing in the fintech industry. And that’s what we are going to discuss in our blog.
AI and ML for credit services and loan disbursement
Banks and other institutions use financial software with artificial intelligence and machine learning solutions that allow them to evaluate the creditworthiness of their customers in real-time so that they can provide instant customer loans. Such software are built using data-led architecture allowing the fintech companies to study the behavior, utility bill payments, social media presence, online purchases, and travel ticketing history of the customers. It helps them design a proxy metric to build alternate measures for checking customer creditworthiness.
The ecosystem of such software uses big data analytics and machine learning in fintech to perform various tasks including picking the ideal frequency for every client, detecting and routing different queries from different channels by designing smart customer support, customizing the communication channels, and identifying the customers that need payment reminder features.
How Machine Learning is used to provide financial services?
Various statistical models are used in the financial industry for financial decision making which is based on machine learning technology and big data. This assists the financial institutions to understand the profiles of their customer in the following two aspects:
- The ability of the customer to pay
- Detect the fraud and prevent loan disbursal to them
Though this is an initiative in the fintech industry, millions of daily active users take advantage of such financial services. The banks and the financial institutions develop the risk and repayment models using machine learning algorithms in this segment. It helps them to determine the true earning capabilities of their customers. Because the customer base may include people working in both organized as well as unorganized sectors, there will be customers who don’t have any regular income patterns or any part cash salaries.
Banks and financial institutions need to determine the income patterns of the customers as equally they must determine the spending patterns, bank credits and debits, utility payments, and other financial transactions of the customers.
And it’s not like that once you have created such a model your work is done. You also need to deploy it and turn it into a self-learning platform that could improve itself over time. So with the ups and down, and all the shocks in the industry, the machine learning algorithm will keep updating your self-learning platform with the help of an end-to-end digital stack.
What kind of tech stack is used to build this platform?
To create and maintain a platform with digital and an instant credit process, the developers must use cutting-edge technologies. This platform would manifest a powerful credit decision engine with a smooth customer journey. The company will gain a strong customer credit portfolio as a product of this combination.
Financial companies have to leverage the power of every single digital innovation to build a self-learning platform. Many financial service providers are seen leveraging a great analytical process with third-party data sources to create and fine-tune their credit and fraud engine.
The tech stack used to build this credit service platform closely resembles the tech stacks for modern consumer products Zomato and Uber than it does for traditional banks or financial firms. You can also host it on the cloud and use an open-source approach.
You can choose from PHP, пітон, and Golang, and many other frameworks to build financial services software. Just make sure that you organize your tech stack in various independent services. And ensure that for every service, you select the programming language, рамкі, database, and architecture that is fit to meet all its needs.
For frontend development, you can either choose Angular or React for the software applications supported in desktops and PCs. Meanwhile, you can use ReactNative to build mobile apps for the faster delivery of your new products and services in the finance industry.
What kind of tech tools need to be used for this project?
A wide range of tools is available in the market that you can use during the development and deployment of a product. Here, in the list below, I have discussed some of the most popular and widely used tools.
- Miro/Invision/Adobe XD – You can convert your ideas into a concept very quickly using these tools. It allows you to debate, discuss, refine and iterate the ideas for the development of any new product or service.
- Slite – You can use this tool for comprehensive documentation of the product. The product-related documents can be easily accessible by other members of your team or the employees of your company.
- Asana – planning the project, taking the sprint progress and product rollouts.
- Postman – One-stop shop for API sharing, API documentation, API monitoring, and API collaboration.
- Sagemaker Studio – You can use this tool with other analytical tools to set up some shareable and repeatable Machine learning systems workflows with no overheads.
- Datadog – It offers monitoring capabilities including 360 degrees monitoring of every production workload.
- Bugsnag – Helps you detect bugs on both the client-side and server-side.
- ELK Stack – It is an open-source tool used for log analysis
- DeployHQ/AWS CodePipeline – To fulfill CI/CD requirements.
- Google Docs/Sheets/Forms
If you wish to offer complete customer satisfaction then along with your in-house tech stack, you can also use several third-party integrations. As for settlements and payments, you can collaborate with traditional banks, online payment gateways, and other financial institutions. You also have to work closely with the financial organizations that help you verify the identity and the credit reports of the customers.
What kind of talent and skills are needed to build it?
Technical skills are a must for the development and deployment of financial software products and services built based on machine learning technology. Still, a financial institution can train employees in the technical aspect. What you should be looking for in the candidate is a strong work ethic, character, passion, perseverance, and the attitude for the hassle. Because such initiatives are full of challenges and if you want to be successful, you need people who are dedicated more than they are educated.
Which technologies have gained more attention in the finance industry amidst the pandemic?
The lockdowns were imposed due to the pandemic of Covid-19 which has boosted the digitalization of businesses around the world. The same is true in the case of the financial industry. Apart from machine learning in fintech, many financial companies and banks are also seen investing in cloud-hosted platforms like the Internet of Things (IoT) and Application Programming Interface (API). If you combine these two technologies then it ought to accelerate innovation and risk mitigation in the digital lending ecosystem.
