From Finance to Health: Application of AI & ML Across Industries
Artificial Intelligence (AI) and Machine Learning (ML) are the most talked about words in any domain.
Did you know that over 60% of business owners believe AI will improve customer relationships?
Machine learning is the process of teaching a machine or a computer to learn from the data without programming it. The machine learns from statistics and algorithms to create and use patterns in decision-making.
However, AI is a broader concept encompassing ML with other approaches to building an intelligent system. It is the ability of a machine or a computer to perform tasks like thinking and reasoning like humans.
Understanding AI & ML Across Industries
AI in Finance
AI and ML are reinventing the finance sector by providing analytical and prediction skills with their algorithms. Finance institutions use these technologies to detect fraud, analyse risks, and develop investment strategies. According to the recent Gartner Survey[1], 39% of organisations have adopted AI to perform their finance functions.
AI in Healthcare
The healthcare sector, too, is aware of AI and ML benefits. Various diagnostic tools to read medical images are being invented to cope with the ever-growing technology. These tools help professionals diagnose their patients quickly and accurately. The global AI healthcare market is projected to be worth almost USD 188 billion by 2030, with a CAGR of 37% from 2022 to 2030.
Usage of AI Across Finance and Health
Let us take a better look at the use of AI in the finance sector.
Algorithmic Trading
The use of algorithms to make better trade decisions is known as algorithmic trading. This is usually done by studying the charts and previous records of the company or index in which one decides to trade. The trader must analyse chart patterns, time, price, and other factors to make a decision on whether to take the trade or not.
Unlike humans, AI, with its algorithmic trading, can analyse large volumes of patterns and make thousands of trades daily. This move is near to accurate and can be beneficial to the trader.
There is no scope for emotions like anxiety or fear in the case of algorithmic trading. The judgment is made statistically.
Fraud Detection and Prevention
Banking and financial institutions go through problems like fraud daily. These can cause them to incur losses of billions of dollars each year. It is a common practice to store vast amounts of data online. This can compromise security.
Traditionally, fraud detection systems were based mainly upon a set of rules that modern fraudsters could crack. This is where machine learning is used. ML scans large data sets and can detect unusual activity. It can inform the security teams to investigate.
AI and ML applications in the healthcare sector have also proven useful. AI is being used across many domains that fall under healthcare. Let us delve into some of the use cases of AI in the healthcare sector.
Portfolio Management
There are AI tools that help in making a perfect portfolio by providing automated advice to investors. The applications use algorithms to establish a portfolio according to an investor’s goals and risks.
These applications are known as Robo-advisors. They are comparatively cheaper than the human advisors.
Prediction and Prevention of Diseases
The contribution of AI & ML in predicting and preventing diseases is noteworthy. The AI technology studies the patients’ data to identify patterns and risks. Such analysis can result in early disease detection and save a patient’s life.
Medical Research
AI & ML can help extensively in finding new drugs. The key is to analyse the data set and repurpose the existing drug. This can be done by analysing the cell images through AI and gathering patterns that show the reaction of cells to various drugs.
Another use can be in the case of clinical trials. A virtual group with a medical database can be created, and the trials can be conducted with the help of an AI application.
Medical Training
AI can help in teaching medical students. It has been found that an AI tutor, with its machine learning algorithms, can teach students safe and effective techniques for performing surgeries. Many institutes have started using AI tutors, especially during the COVID-19 pandemic.
AI shows several skills, such as comforting a patient in distress or delivering bad news.
Benefits of Adopting AI & ML
AI and ML are more than just supplementary tools to analyse and detect. They can be largely beneficial in many ways.
Increased Efficiency
AI ML technologies are known for automating repetitive tasks. Automation in daily tasks will improve AI accuracy and resolve the wastage of human resources, thus increasing efficiency.
Finance and health industries can see an extensive increase in efficiency due to the automation of tasks. Automation in finance can help in routine tasks like data entry and reconciliation, thus freeing up human resources. These resources can be used more in strategic roles.
In healthcare, the administrative tasks are automated so that the professionals can focus more on the patients.
Improved Decision-Making
AI and ML are continuously updating. The reasoning ability is increasing each day, making it a more decisive system across all the domains.
Processing large amounts of data in real time helps AI make informed decisions. Financial institutions can use AI's ability to make predictions. These predictions help them to formulate investment strategies.
Healthcare providers, on the other hand, can use the insights gathered by AI from data sets to personalise the patient’s treatment and improve outcomes.
Faster Data Processing
AI ML technologies can handle massive datasets in real time. This improves efficiency and ensures faster data processing.
Analysing market trends and various trading data will give you an edge in algorithmic trading. Handling vast information becomes easy with AI.
In the healthcare business, the responses to customer interactions are quicker and help save much time. AI can help in reading and understanding vast amounts of patient reports.
Challenges in Implementing AI & ML
As we have seen all the benefits of AI & ML, there are challenges while implementing the same.
Security and Privacy Concerns
The challenge of securing data and ensuring privacy increases as industries become more dependent on AI & ML. Many industries, like defence, schools, etc., store sensitive data online. These pose a security threat. Financial and health data are sensitive. Breach of such data can have serious consequences. Maintaining a balance between innovation and security is a challenge.
Ethical Dilemmas
AI uses human datasets to learn. These datasets can be biased, resulting in prejudiced algorithms. It may compromise fairness.
Using AI in the decision-making process can cause ethical dilemmas. In finance, algorithmic trading may create a bias and not show the real situation of the market. It can hamper an investor's sound decision-making abilities.
In healthcare, AI-generated reports must be considered carefully and regulated with the patient’s consent.
Conclusion
To sum up, applications of ML and AI are being adopted widely across all industries, with finance and health leading the way. Though there are several benefits of implementing AI and ML, some challenges need to be tackled. Challenges like security, privacy, and ethical dilemmas must be handled with utmost care. As these technologies evolve, finding the right balance between innovation and responsibility for a better future is crucial.
Transforming your business with technological solutions becomes easy with Growth Jockey. We nurture businesses, enabling them to scale successfully. Our digital advertising, tech innovations and operations management will help your business thrive. We incorporate powerful AI & ML tools to foster growth.
FAQs
1. Do AI & ML help in financial decision-making?
Yes, AI and ML applications enhance financial decision-making. The tools can predict and assess the risk and help in market analysis. These technologies enable accurate investment strategies.
2. Can AI predict diseases?
Yes, AI can analyse patient data to predict diseases. This helps in taking preventive measures. Early detection of a disease can save a patient’s life.
3. What are the challenges faced while implementing AI & ML?
Businesses face challenges such as data security concerns and the need for a professional to develop such systems.
4. How do AI & ML increase efficiency in finance?
In finance, the automation of tasks is done by AI and ML. Routine tasks such as data entry and reconciliation are automated, leading to increased efficiency. This allows using human resources in more useful tasks like strategic decision-making.
5. Are there any regulating authorities that govern the use of AI in the finance and healthcare sectors?
Many countries are developing frameworks to govern the use of AI. However, there is a need for continued efforts to address ethical and data privacy concerns.
6. What is the future of AI & ML in the finance and healthcare industries?
The future is very promising. There are many advancements in the applications of ML and AI. The finance and healthcare sectors are expected to increase usage, leading to more personalised services and improved results.
Industries
Growth
Operations
Technology