By Yevhen Krasnokutsky, AI/ML Solution Architect at MobiDev
Fintech startups are flourishing since our lives shifted to an online format. Statista reveals that as of November 2021, there were 10,755 startups in the Americas alone.
This number is still growing in 2022, and that’s why traditional financial companies encounter difficulties trying to withstand competition and remain effective. By adopting machine learning solutions, such companies can optimize main business processes and foster customer loyalty.
So, in this article, we’re going to lift the curtain on machine learning (ML) approaches that enable process automation.
Streamlined Customer Onboarding Process
The customer onboarding process should be performed in a short time and at a minimum cost to ensure great customer experience and economic efficiency of a business.
But in practice, it takes from 2 to 34 weeks to onboard new clients, and the whole process is closely associated with the maturity of the bank or other financial firm. AI comes in handy here because ML enables process automation while enhancing customer experience.
How does it work?
ML algorithms enable the smooth functioning of the digital onboarding process in the following ways:
- Photo verification – a client’s photo is being checked across all documents. Axis Bank, DBS, and other financial institutions have already implemented forensic image analysis.
- Accurate document conversion – using natural language processing (NLP) along with optical character recognition (OCR), banks can easily convert downloaded documents into digital ones and eliminate the need to process documents manually.
- Support of the client’s virtual journey – chatbots trained on large datasets may perform actions required for onboarding of new customers, which has already proven its efficiency through the example of British banking heavyweights RBS and HSBC.
By adding innovative fintech features to software, traditional banks can stand out in the market and improve customer retention. Using ML for customer onboarding is just one of many examples.
Automated Document Data Analysis
Having a chance to automate document processing is of paramount importance for the whole financial industry – valuable usage scenarios include the digitalization of the audit process, input error identification, and financial performance scanning.
Subsets of AI applied to analyze documents are computer vision and deep learning, while particular solutions can be crafted step-by-step, in the following sequence:
- Clarify business needs and define how automated document data analysis augments existing processes and enhances a workflow
- Determine the desired functionality that may influence what data – from financial figures to keywords – are crucial for the model and whether this information corresponds to the business logic
- Train the ML model on structured datasets obtained in the previous stage
Machine learning models help banks and other financial institutions to reduce human errors, prioritize document review, and generate valuable insights.
Improved Fraud Detection
Any financial ecosystem must comply with security regulations and be highly resistant to fraud. That’s why financial institutions implement fraud detection systems, which can be based on traditional methodologies with manual data assessment (rule-based fraud detection) or ML algorithms.
Machine learning has proven to be an efficient tool for evaluating enormous datasets in the era of the shifting fraud landscape. Sophisticated models are capable of finding hidden patterns and detecting anomalous behavior in the blink of an eye.
When fed with more data, such systems become even more accurate. In addition, ML allows for improving user experience by reducing the number of verification steps required when performing a transaction. Biometric security systems with face detection or voice verification may serve as an example here.
Enhanced Customer Support
With conversational AI, robo advisors, and identity verification via face or voice recognition, which nurture a better customer experience, becomes much easier.
Let’s start with conversational AI, the branch represented by various chatbots and virtual assistants which help to resolve clients’ needs and technical issues.
When conversational AI comes into play, financial businesses can facilitate customer support. The system deciphers the meaning of the customer’s need, converts voice into text, and then answers the query or connects the client with the right employee at the help desk. This approach reduces the workload and expenses for the customer support department.
At the same time, financial companies leverage customer-related data to build relevant profiles and meet clients’ expectations. AI allows for supporting omnichannel strategy and providing positive customer experience across different platforms.
As for robo advisors, they are attracting a lot of attention nowadays, helping to invest more competently, taking into account risks, market trends, and even the environmental sustainability of the companies.
Companies that connect their activities with trading and investments will certainly want to have such a solution in their arsenal.
More Accurate Financial Forecasting
Using ML in financial forecasting helps to make accurate predictions by quickly analyzing available data and determining drivers or patterns.
There are no limitations in the volume of structured and unstructured data to be analyzed, so it’s possible to find more insights.
The whole process can’t be performed only by engineers and involves finance executives and analysts who should find out the drivers of the particular business.
These drivers form the basis of hypotheses that should be determined before building the machine learning forecasting model.
The combined experience of financial specialists with the ability to quickly process large volumes of data using machine learning significantly improves the accuracy of financial forecasts and enhances business intelligence.
ML implementation seems to be the first step toward the digitalization of financial companies. The financial industry has access to a significant amount of consumer data, which simplifies the creation of ML models.
However, to reap benefits from AI adoption, companies should clearly understand key KPIs and match technology capabilities with their business goals.
Also, don’t forget that in order to achieve valuable outcomes, the technology must be properly integrated into the product. Therefore, enlisting the support of experienced AI engineers who know all the specifics of incorporating AI into an app will help you achieve your goals in the most efficient way.