Machine Learning in Finance – Overview, Applications, Automation

Machine learning in the financial sector may work magic, while there is no magic behind it. Still, ML project success depends on building efficient infrastructure, gathering the right datasets and applying the right algorithms.

Updated 20 September 2024

Chandrapal Singh
Chandrapal Singh

Director at Appventurez

Machine learning (ML) is transforming the financial sector, offering innovative solutions to complex problems. While there’s no “magic” behind its success, the effectiveness of any ML project depends on three critical factors: building a robust infrastructure, gathering high-quality datasets, and applying the right algorithms.

A prime example of AI and ML-powered innovation in finance is Cleo, a messenger-based financial assistant. Cleo uses artificial intelligence solution to help users manage their transactions, showcasing the potential of ML in delivering personalized financial services.

The role of machine learning in finance is becoming increasingly pivotal, particularly in the automation of financial processes. Let’s explore the challenges companies face when implementing machine learning solutions, and how combining ML with robotic process automation (RPA) can create more efficient systems.

Key Challenges in Adopting Machine Learning in Finance

  1. What obstacles do financial companies encounter when integrating ML solutions?
  2. Why is it crucial to pair machine learning with robotic process automation (RPA) for optimal results?

Surprising Insights on Machine Learning in Financial Automation

Machine learning is driving significant change in the finance industry, offering high-complexity solutions that deliver strong returns on investment (ROI) across various business segments. Here are some noteworthy facts about ML’s impact in finance:

  1. 56% of financial services companies have implemented ML technologies in areas such as risk management, while 52% are using ML to generate revenue through new products and processes.
  2. By 2023, AI applications are expected to save the banking industry a staggering $447 billion, thanks to AI-driven intelligence in operations.
  3. To remain competitive by 2024, financial institutions must integrate AI and ML solutions into their core processes. Mobile and web banking adoption in the U.S. is projected to reach 72.8% and 58.1% of users, respectively.

The integration of machine learning and AI is no longer optional but essential for staying ahead in the fast-evolving financial landscape.

impact of machine learning solution in fintech innovations

It is also noticed that ML technology is rapidly implemented in the banking sector. 75% of respondents at banks with over $100bn in assets say that they are currently deploying AI & ML technologies. AI-based apps offer the most significant cost savings chances across digital banking.

The Impact of Machine Learning Solutions on the Finance Industry

Machine Learning (ML) offers transformative benefits to the finance sector, where large volumes of data drive decision-making and operations. With the right ML algorithms and datasets, the financial and banking industries can unlock numerous opportunities presented by AI and ML technologies. Here are the key impacts of ML on the finance industry:

  • Automation

Those days are gone when paper workflows were effective. Today, ML-powered models enable efficient document sharing, editing, storage, and management. These intelligent systems significantly reduce the time and cost associated with handling documents, streamlining operations for faster, more accurate results.

  • Productivity

AI and ML excel at automating monotonous, repetitive tasks—activities that can consume up to 60% of employees’ time. By leveraging ML algorithms, businesses can offload these tedious tasks, allowing employees to focus on higher-value work and strategic business objectives.

How Workflow Automation Assists Businesses

Source: flair.hr

  • Operational Cost Reduction

ML solutions minimize human error and reduce operational costs by outsourcing specific tasks to machines. Automating processes that were once manual improves accuracy and efficiency, leading to better financial performance.

  • Security and Compliance

With strict adherence to safety protocols, AI and ML enhance security measures and ensure better compliance within the financial sector. These technologies can monitor for threats, detect anomalies, and ensure data integrity, safeguarding businesses and their customers.

  • Enhanced Customer Experience

Losing a single customer may seem trivial, but poor communication and unresolved issues can erode a significant portion of a customer base over time. AI and ML dramatically reduce the time needed to retrieve information and resolve customer concerns—from several days to just minutes. Offering seamless 24/7 customer service with AI-powered virtual assistants demonstrates a company’s commitment to its customers and improves overall satisfaction.

  • Personalization

ML algorithms enable businesses to analyze vast amounts of customer data, tailoring services to meet individual preferences and needs. For instance, if a customer purchases a property, you can offer them insurance. If they plan to start a business, you can recommend a new bank account.

By assessing the financial health of accounts, ML provides personalized investment insights, enhancing the customer experience while driving business growth.

In addition to these impacts, ML-driven robotic process automation (RPA) is advancing beyond simple task execution. ML enhances robots’ capabilities, enabling them to perform tasks that traditionally required human decision-making.

These systems can improve data integrity, provide deeper business insights, and optimize automation processes.

The finance industry stands to gain significantly from the integration of ML solutions, which not only streamline operations but also drive personalized customer experiences, improve security, and reduce operational costs.

As AI and ML technologies continue to evolve, their potential applications in the financial sector will only grow, paving the way for more innovation and efficiency.

Looking to Hire a Machine Learning Developer to Automate Your Business

How to Integrate Machine Learning Systems with Robotic Process Automation (RPA)

Machine learning (ML) is revolutionizing how businesses implement automation. Here are key ways ML enhances automation through Robotic Process Automation (RPA):

  • Handling Unstructured Data

Around 80% of business data is unstructured, including images, audio files, text documents, PDFs, and customer service emails. Machine learning, combined with cognitive technologies like Optical Character Recognition (OCR) and Natural Language Processing (NLP), transforms this unstructured data into a structured format that can be automated.

  1. OCR: OCR is highly effective for identifying, extracting, and categorizing data from scanned images and documents.
  2. NLP: NLP can be trained to analyze and interpret emotions in unstructured text from customer emails, chats, and voice inputs, enabling better automation decisions.
  • Enhancing Automation Efficiency

Machine learning algorithms significantly improve the execution of automation tasks. For example, computer vision enables robots to interact with on-screen components by learning from visual data, optimizing processes like data entry and system navigation.

Additionally, ML algorithms can reduce code complexity, leading to faster runtimes and more efficient automation.

Another application is in task assignment: ML can analyze routine tasks performed by employees, generate process maps, and suggest the most suitable tasks for automation based on Return on Investment (ROI).

  • Attended Automation: Human-Robot Collaboration

Attended automation, often referred to as remote desktop automation, involves robots working alongside humans to enhance decision-making and improve task outcomes.

Machine learning enables these robots to process data from multiple sources in real time, providing humans with insights on the best next steps in their workflow.

By combining ML with cognitive technologies like NLP, robots can even mimic simple human decision-making processes, leading to seamless, end-to-end automation.

Use Cases of Automated Machine Learning Systems in Finance and Banking

  • Machine Learning for Fraud Detection

Automated machine learning offers several advantages in enhancing security, particularly in fraud detection. Some key benefits include:

    1. Reducing the risk of data breaches and cyberattacks: By continuously monitoring for fraudulent activity, ML systems can effectively mitigate risks.
    2. Real-time security tracking: These systems operate with minimal human intervention, providing seamless and efficient security management.
    3. Easier identification of fraudulent transactions: Machine learning algorithms can quickly detect suspicious transactions, enhancing the accuracy and speed of fraud prevention.
  • Notable Startups Leveraging ML for Fraud Detection

    1. Resistant.ai: Based in Prague, this startup specializes in protecting AI systems from manipulation while offering advanced fraud detection capabilities.
    2. Riskified: Utilizes machine learning algorithms to analyze payment data, helping businesses decide whether to accept or reject transactions securely.
    3. Feedzai: Focuses on using ML to create risk management tools that prevent fraudulent transactions and enhance security in financial operations.
  • Machine Learning for Customer Retention

Machine learning is a powerful tool for improving customer retention through enhanced personalization, data analysis, and process automation. However, to achieve greater speed and interactivity, banks are increasingly turning to chatbot development solutions.

  • Leading ML-Powered Chatbots in Banking

    1. Wells Fargo: As the first U.S. bank to integrate an ML-powered virtual assistant into Facebook Messenger, Wells Fargo used this system to provide personalized customer service and enhance engagement.
    2. Bank of America: A pioneer in offering an ML-enabled virtual assistant, the bank introduced “Erica” within its mobile app, providing personalized banking solutions and improving the customer experience.
  • Machine Learning in Stock Market Forecasting

Machine learning has proven highly effective in forecasting stock market trends. Some of the primary benefits include:

    1. Efficient data processing: ML algorithms can analyze large volumes of stock data in real time, identifying patterns and anomalies.
    2. Improved prediction accuracy: ML can detect subtle correlations that humans might overlook, making predictions more reliable.
    3. High reliability: Predictions made by ML models often surpass human accuracy due to their ability to process vast amounts of data continuously.
  • Startups Using ML for Stock Market Forecasting

    1. Walnut Algorithms: Specializes in applying AI and ML solutions for investment management, enabling more informed and data-driven decisions.
    2. QARA: Uses deep learning technology to predict financial markets and has developed a mobile app to make these insights accessible to investors.
  • Machine Learning for Meeting Compliance Regulations

In the finance sector, staying compliant with ever-changing regulations can be time-consuming and costly. Machine learning can streamline this process by:

    1. Automating compliance monitoring: ML can track regulatory changes in real-time and adjust business processes accordingly.
    2. Reducing manual efforts: By automating routine regulatory tasks, ML helps financial institutions save time and reduce human errors in compliance-related activities.
  • Startups Addressing Compliance with ML

    1. Zest.ai: Helps financial institutions make better lending decisions by automating compliance, improving loan management, and minimizing risk.
    2. Sym: Focuses on closing the gap between compliance policies and workflows, ensuring smoother and more efficient regulatory management.

How Appventurez Empowers Machine Learning Development?

Unlock new opportunities, streamline operations, and achieve exceptional business outcomes with customized machine learning solutions tailored by Appventurez, a leading machine learning app development company. Our expert team delivers forward-thinking ML-integrated solutions that help businesses in:

  1. Accelerating decision-making
  2. Sales forecasting
  3. Predictive modeling
  4. Big data management

Why Choose Appventurez for Machine Learning Development?

Appventurez is a most trusted application development company in India, empowering businesses to reach new heights by enhancing workflow efficiency and reducing operational costs. Here’s why partnering with us for AI & ML development can transform your business:

  • Comprehensive Requirement Analysis

We thoroughly evaluate your project needs, aligning them with a highly skilled team of certified ML developers to ensure precise, custom-built solutions.

  • Results-Oriented Solutions

Our ML-powered applications are designed to deliver actionable insights and fast results, enabling quicker turnarounds and measurable impact on business growth.

  • Security and Scalability

Benefit from state-of-the-art data encryption and advanced security protocols, ensuring your data is safeguarded against potential threats while allowing for seamless scalability.

  • Agile Development Approach

Our cross-functional teams employ an agile methodology, ensuring collaboration at every stage to deliver flexible, efficient, and robust software solutions.

Planning to Implement AI & ML into your FIntech Domain

FAQs

Q. What challenges are faced by finance companies when implementing machine learning solutions?

ML helps organizations make sense of their data, automate business processes and enhance productivity. When companies want to adopt machine learning algorithms, they often find themselves struggling to start the journey. Here are some issues which companies face during the machine learning adoption process. * Inaccessible data security * Infrastructure requirements for testing & experimentation * Rigid business models * Lack of talent * Time-consuming implementation

Q. Why Should You Use A Machine Learning System With A Robotic Automation Process?

The limitless quantity of available data, affordable data storage & more powerful processing has driven ML growth. Many companies are building more robust models capable of identifying big & complicated data when delivering faster & more accurate results on a huge scale. Machine learning enables companies to identify profitable chances & potential risks quickly. 

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    Chandrapal Singh
    Chandrapal Singh

    Director at Appventurez

    Director and one of the Co-founders at Appventurez, Chandrapal Singh has 10+ years of experience in iOS app development. He captains client coordination and product delivery management. He also prepares preemptive requisites and guides the team for any possible issues on a given project.