Deep Learning and Machine Learning Applications in Financial Services

Introduction

In today’s financial world, the FinTech industry (a financial industry using disruptive technologies) leverages machine learning (ML) and deep learning (DL) technologies to solve problems and create improved financial solutions, thereby improving the customer experience. These technologies help improve customer-centric services such as managing assets, evaluating risks, calculating credit scores, and so on much more efficiently than the traditional approach.

For example, customer analytics can help financial institutions better understand customer behaviour and preferences.

Customer analytics segments (groups) customer data based on criteria such as demographics, transaction behaviour, product usage, and engagement patterns. These insights can help financial institutions tailor their products and services to meet the specific needs and preferences of every customer group.

Applications include federated learning for privacy-preserving data analysis in distributed financial systems, sentiment analysis using natural language processing, and generating synthetic data using generative models (GANs).

Why does machine learning fit financial applications?

Financial services have access to large amounts of structured and unstructured data, such as daily transactions, bills, payments, vendors, and customers, making it a strong sector for machine learning applications. Incorporating machine learning into financial operations results in improved consumer experiences, streamlined processes, reduced risks, and optimised portfolios.

In this article, you will understand various use cases for Machine learning and Deep learning in the financial services sector. You will also see the various challenges faced by the FinTech industry while using ML and DL.

Why do you need machine learning for FinTech?

FinTech is a combination of finance and technology. Incorporating machine learning into finance improves the performance and cost-efficiency of the organisation, thereby enhancing business processes and helping to make informed decisions. Machine learning uses large amounts of financial data that is collected through customers, markets, historical data, trends, etc. to analyse and provide insights about financial trends, uncover important data patterns, and improve the accuracy of predictions.

Financial institutions use machine learning to offer better pricing, mitigate risks associated with human error, automate repetitive tasks, and understand consumer behaviour.

Note: You can implement various types of ML, such as reinforcement learning and supervised and unsupervised learning, in Fintech.

What is machine learning?

Machine learning is a subfield of artificial intelligence (although the terms machine learning and artificial intelligence are used interchangeably) that provides machines with the ability to learn without being explicitly programmed. Machine learning, also known as ML, is used in multiple applications, such as Gmail (to filter spam from important messages), shopping applications (to recommend relevant products based on your previous purchases and search history), the stock market (to predict upcoming stocks based on historical data), and so on.

What is deep learning?

Deep learning (DL) is a technique associated with ML that teaches machines to perform what humans do naturally, such as recognise patterns in text, sound, images, and so on. Deep learning can be used to automate tasks that typically require human intervention and intelligence. DL uses neural networks to mimic the structure and function of the brain by teaching machines to process data inspired by the human brain.

Applications of machine learning and artificial intelligence in the field of FinTech include:

Loan risk evaluation
Customer analytics
Pricing and offers
Claims adjudication
Flagging fraud
Economic modelling
Stock market predictions
New product creation
Operational efficiencies
Insurance Underwriting

Challenges faced in Fintech with ML usage

With the advent of digitization and its utility in the FinTech industry, it is essential to make unbiased predictions. But the algorithms may fail to produce unbiased results if they are not developed with an unbiased mind, are untested, or are solely based on historically biased data. Therefore, it is important to have data handling policies and regulatory guidelines to avoid and mitigate cyber threats.

A report in 2020 suggested that Apple cards gave about 20 times less credit to women since the algorithm used was untested and used biased data.

This indicates the importance of building an ethical framework that is essential to developing ML algorithms for Fintech.

Some of the challenges faced by the FinTech industry in the adoption of ML and DL have been discussed below.

Regulatory requirements

Regulation protects consumers and ensures payment safety, which is an integral aspect of fintech regulation. The regulatory requirements should mitigate the risk of money laundering and terrorist financing. Every FinTech organisation should have the ability to achieve financial compliance using customised measures and controls.

Interpretability and explainability of machine learning models are important aspects of gaining customer trust since they justify and help understand the decisions taken by ML models.

This is a challenge since every firm is different, and it is required to implement unique solutions for the needs of the firm. That said, implementing these regulatory measures ensures the safety of consumers, establishes trust between firms and their customers, stimulates market competition, and facilitates company growth.

2. Data access and governance

New, innovative business models need to establish a robust data governance framework that is both essential and challenging. Such a framework is responsible for the organisation’s data management process, data architecture and models, and data repositories.

Data governance helps ensure that the data used by ML models are not malicious or misleading; otherwise, it may adversely result in biased decisions, leading to inaccurate predictions.

3. Model governance

Model governance determines how an organisation controls access to data associated with building the model and implements policy and activity tracking for models and their results. It includes setting access control to models in production, versioning models, documenting them, and monitoring the models and their results.

It is important to ensure that models used in the Fintech industry adhere to certain policies, thereby avoiding the misuse of the models, their results, and the data associated with them.

4. Complex technology ecosystem

Fintech ecosystems are always evolving with the continuous development and adoption of technology. Such an ecosystem is considered one of the most complex systems since it is a collection of multiple technologies working synchronously to achieve a common goal. Building and maintaining such an ecosystem is a daunting yet essential task since it improves service and product quality.

5. Model validation

Validating the model built is essential to determining its accuracy, susceptibility to bias (such as unfairly ignoring or targeting specific zip codes, titles, or gender), risk, reliability, and so on. It is important to evaluate the model as part of the regulatory scrutiny to avoid biases and, consequently, being liable to consumers and regulators.

6. Model deployment

After validating a model, it can be deployed by setting it up in a production environment. In such an environment, real-time data is processed and predictions are made. Depending on your infrastructure, a production environment can encompass anything from a cloud-based platform to an on-premise server or a service that users can directly consume on their devices.

7. Model monitoring

Once the model has been deployed, it is essential to monitor its performance continuously to determine its accuracy and performance metrics. It also helps address and resolve issues that arise during continuous monitoring.

8. Security and privacy

Due to the sensitive nature of financial data and the potential impact of security breaches, it is important to handle data fed to ML models properly to avoid privacy violations.

Conclusion

In this article, you learned about the importance of machine learning and deep learning in the field of FinTech. You also learned about the various applications of ML and DL in Fintech and the challenges faced when adopting these technologies. ML and DL provide a data-driven approach to understanding customers better by analysing and interpreting customer data and building strong relationships with their clientele. As a consequence, there is increased customer loyalty, higher retention rates, and a competitive advantage in the financial service market.