Machine Learning Applications in Healthcare and Finance

The rapid growth in the number of applications for machine learning (ML) in healthcare and finance has created a tremendous amount of opportunity for organizations looking to enhance workplace efficiency, productivity per worker, and overall output. The market for AI in healthcare alone is projected to grow from just over $26.5 billion in 2024 to nearly $188 billion in less than a decade.¹ Similarly, spending on AI in the financial services industry totaled $35 billion in 2023 and is projected to reach $97 billion by 2027.²

It’s little wonder that AI adoption has taken off with particular gusto in these sectors. Not only can AI boost productivity, but in healthcare settings, it can actually save lives. Meanwhile, for firms dealing with complex financial models, AI can help to mitigate risk.

Read on to learn more about how AI adoption is transforming the fields of healthcare and finance.

Key ML Techniques

Supervised learning currently dominates applications. By learning from tailored datasets, powerful AI models have taken over a broad range of tasks in the financial services and healthcare fields, from credit risk assessment to medical image classification. Support vector machines and random forest algorithms, in particular, excel at financial credit scoring, while neural networks have achieved high accuracy rates for diagnostic imaging.³

Unsupervised learning has enabled banks and hospitals to segment customers according to details that might otherwise remain hidden in complex datasets, allowing for better, more accurate, and more helpful services. Finally, deep learning tools, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), have revolutionized the speed and efficacy with which hospitals and financial markets can analyze large quantities of data.

Reinforcement learning can be used across industries for a variety of purposes. For example, it has applications for treatment protocol and trading strategy optimization. The breadth of techniques available allows finance and healthcare organizations to tailor their AI and ML usage to suit their unique needs.

Healthcare Applications

Advances in the use of AI in the field of medical imaging diagnostics have grown by leaps and bounds, with an increasing number of AI-powered devices gaining FDA approval in recent years.⁴ These new technologies can increase diagnostic accuracy and consistency, as well as detect features that humans may be prone to miss.³

Applications of predictive analytics in medicine have also yielded a high return on investment (ROI). For example, Corewell Health used AI-powered risk stratification to improve patient health outcomes, preventing 200 patient readmissions and saving $5 million.⁵

Remote monitoring and wearable technologies represent another area that’s seen explosive growth. The remote patient monitoring market is projected to roughly triple in size from 2023 to 2028.⁶ These systems can help reduce the number of hospital admissions through advanced tracking and prevention. AI also enables easier analysis of new techniques and drug trials, making treatment and recovery faster and more reliable.

Finance Applications

Financial firms have increasingly turned to AI to help fight fraud with remarkable results. For example, AI tools allowed the U.S. Treasury to recover over $4 billion in 2024—several billion dollars more than was recovered in 2023.⁷ Financial forecasting with machine learning has also seen strong growth, with algorithmic trading markets reaching $21 billion in 2024. This figure is expected to jump to nearly $43 billion by 2030.⁸

The finance industry has also found strong applicability for customer segmentation and personalized financial advice. Emblematic of this surge is the more than 26 billion digital interactions with Bank of America’s “Erica” virtual assistant tool.⁹ Customers appreciate the personalized touch offered by AI, while corporations and banks benefit from its ability to monitor anomalies and assist with regulatory compliance.

Ethical and Regulatory Considerations

The regulatory landscape is evolving to meet the new challenges posed by AI in fields like healthcare. For example, the EU AI Act created explicit requirements for high-risk AI systems, including medical devices.¹⁰

Important ethical considerations, such as bias detection and mitigation, have become crucial areas of focus. Concerns about possible racial biases that might cause an algorithm to misdiagnose patients of color, for example, have caused firms to take a second look at the datasets used to train their machine learning models. Financial services firms face similar challenges due to the sensitive nature of their work and the risk of biased data being included in their datasets. When using AI to make critical decisions about a person’s life or livelihood, it’s critically important to be aware of its potential fallibility.

Success Metrics and Return on Investment

Organizations implementing machine learning solutions in healthcare and finance are seeing substantial returns on their investments, with measurable improvements across multiple key performance indicators.

Clinical Outcomes and Healthcare ROI

Healthcare organizations are tracking success through concrete clinical outcomes that directly impact patient well-being and organizational efficiency. In addition to the reduced readmission rates reported by organizations like Corewell Health, healthcare providers are measuring diagnostic accuracy improvements, with some AI-powered imaging systems showing error reduction rates of 20-30% compared to traditional methods.¹¹

Cost savings represent another critical success metric, encompassing everything from reduced administrative overhead to optimized resource allocation. Hospitals can implement ML solutions in various ways to reduce operational costs by up to 30%,¹² while emergency departments using predictive analytics have been able to improve triage and “door-to-doctor times” by 15-30%.¹³ These efficiency gains translate directly to improved patient satisfaction scores and better clinical outcomes.

Financial Services Performance Indicators

Financial institutions measure ML success through traditional performance metrics enhanced by artificial intelligence capabilities. Lower default rates stand out as a primary indicator, with ML-powered credit scoring systems reducing loan defaults by 15-20% compared to conventional assessment methods.¹⁴

Operational efficiency gains in financial services are equally impressive. Fraud detection systems powered by ML algorithms have increased detection rates while reducing false positives by up to 50%, significantly lowering investigation costs and improving customer experience.¹⁵

Lead Industry Innovation With Machine Learning  

Machine learning applications can yield significant benefits for organizations of all sizes, particularly in industries like healthcare and finance. The Online Master’s in Data Science from New York Institute of Technology can equip you with the technical skills and deep knowledge of machine learning you’ll need to lead innovation efforts with confidence.

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