A quick guide to automating financial processes with machine learning

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By Yevhen Krasnokutsky, AI/ML Solutions Architect at MobiDev

Fintech startups are flourishing since our lives shifted to online format. Statista reveals that in November 2021 there were 10,755 startups in the Americas alone.

This number continues to grow in 2022, which is why traditional financial companies are finding it difficult to compete and remain efficient. By adopting machine learning solutions, these companies can optimize key business processes and build customer loyalty.

So, in this article, we will lift the curtain on machine learning (ML) approaches that enable process automation.

Simplified customer onboarding process

The customer onboarding process should be done in a short time and at minimum cost to ensure excellent customer experience and economic efficiency of a business.

But in practice, it takes 2 to 34 weeks to onboard new customers, and the whole process is closely related to the maturity of the bank or other financial company. AI is helpful here because ML enables process automation while improving customer experience.

How it works?

ML algorithms enable the smooth operation of the numerical integration process in the following ways:

  • Photo Verification – a client’s photo is verified on all documents. Axis Bank, DBS and other financial institutions have already implemented forensic image analysis.
  • Accurate document conversion – using Natural Language Processing (NLP) as well as Optical Character Recognition (OCR), banks can easily convert uploaded documents into digital documents and eliminate the need to process documents manually.
  • Accompaniment of the customer’s virtual journey – chatbots trained on large datasets can perform actions necessary for onboarding new customers, which has already proven its effectiveness 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 loyalty. Using ML for customer onboarding is just one example of many.

Automated document data analysis

Having the ability to automate document processing is critically important to the entire financial industry – valuable use cases include digitizing the audit process, identifying input errors and analyzing financial performance.

The subsets of AI applied to document analysis are computer vision and deep learning, while particular solutions can be developed step by step, in the following order:

  1. Clarify business needs and define how automated analysis of document data augments existing processes and improves a workflow
  2. Determine the desired functionality that can influence which data – from financial figures to keywords – is crucial to the model and whether this information matches the business logic
  3. Train the ML model on structured datasets obtained in the previous step

Machine learning models help banks and other financial institutions reduce human error, prioritize document review, and generate valuable insights.

Improved fraud detection

Any financial ecosystem must comply with security rules and be highly resistant to fraud. This is why financial institutions are implementing fraud detection systems, which can be based on traditional methodologies with manual data evaluation (rule-based fraud detection) or ML algorithms.

Machine learning has proven to be an effective tool for evaluating huge data sets in an era of changing fraud landscape. Sophisticated models are able to find hidden patterns and detect abnormal behavior at a glance.

When fed with more data, these systems become even more accurate. Additionally, ML helps improve the user experience by reducing the number of verification steps required when executing a transaction. Biometric security systems with face detection or voice verification can serve as an example here.

Improved customer support

With conversational AI, robo-advisors and identity verification through facial or voice recognition, which promote a better customer experience, becomes much easier.

Let’s start with conversational AI, the branch represented by various chatbots and virtual assistants that help solve customer needs and technical problems.

When conversational AI comes into play, financial firms can make customer support easier. The system deciphers the meaning of the customer’s need, converts voice to text, and then either answers the query or connects the customer to the right helpdesk employee. This approach reduces the workload and expense of the customer support department.

At the same time, financial companies are leveraging customer data to create relevant profiles and meet customer expectations. AI helps support the omnichannel strategy and deliver a positive customer experience across different platforms.

As for robo-advisors, they are attracting a lot of attention these days, helping to invest more competently, taking into account risks, market trends and even the environmental sustainability of companies.

Companies that combine their activities with trading and investments will definitely want to have such a solution in their arsenal.

More accurate financial forecasts

Using ML in financial forecasting enables accurate predictions by quickly analyzing available data and determining drivers or patterns.

There are no limits to the volume of structured and unstructured data to analyze, so more information can be found.

The whole process cannot be done by engineers alone and involves finance executives and analysts who need to uncover the drivers of the business in question.

These drivers form the basis of assumptions that should be determined before building the machine learning prediction model.

The combined experience of financial specialists with the ability to quickly process large volumes of data using machine learning dramatically improves the accuracy of financial forecasts and enhances business intelligence.

Wrap

The implementation of ML seems to be the first step towards the digitization of financial companies. The financial sector has access to a significant amount of consumer data, which simplifies the creation of ML models.

However, to capitalize on the adoption of AI, companies need to clearly understand key KPIs and match technology capabilities to their business goals.

Also, keep in mind that to get valuable results, the technology must be properly integrated into the product. Therefore, enlisting the support of experienced AI engineers who know all the specifics of integrate AI into an application will help you achieve your goals in the most efficient way.

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