Machine Learning Defined
What is Artificial intelligence? In simple terms, it represents intelligence exhibited by machines. Machine learning is one of the most successful approaches to realizing AI. In fact, some researchers believe that it is the key to True AI. Furthermore, deep learning originated from neural networks, a classic machine learning algorithm modelled on the human brain and nervous system. Now it is a powerful toolbox in machine learning. Recent successes of machine learning in application areas are driven by deep learning, such as image and speech recognition.
Deep Learning Explained
Deep learning significantly reduces the challenges around building an in-house solution. This is because the framework is very standardized and the software for such a framework is available for free. For instance, many experienced PhDs have spent years building a facial recognition system that could achieve 80% accuracy and have published thousands of research papers. Now, a bachelor with basic programming skills can build a 90% accuracy system in a day by reading a blog post. By learning some slightly more advanced techniques, the system can be further improved to roughly 99% accuracy. This was nearly impossible ten years ago, even for the most successful researchers and industry practitioners in the field.
How Industry Giants Use AI
We have witnessed a huge leap in the progress of AI technologies for the past several years. Google’s AlphaGO, self-driving cars, and cancer diagnosis assistants, is now one of the more promising trends in the financial industry. Big-name companies have been utilizing AI for years, including NFL (National Football League), BMW, Netflix, and more. Netflix, one of the world’s most popular movie streaming services on the planet, uses AI to determine the best possible recommendations for users, while similarly Amazon relies on machine learning to recommend specific products based on what users have already purchased or searched for. Machine learning managed to outperform humans on many well-defined problems. machine learning now plays an important role in some more complicated systems such as Siri or Google Now. Siri, for example, is utilizes speech recognition and speech synthesis
, and other machine learning tech to listen and respond to very specific user inquiries. Machine learning also provides the fuel to related areas such as data mining. For example, when you are looking for something in Google or Amazon, you may have personally customized results that differ from others. Machine learning is not only for the high-tech internet companies, but also for many established industries, such as drug designs in medicine, automatic summarizing of documents in legal, and robo-advisors in trading.
Top Benefits of AI and Machine Learning
To recap, the purpose of AI is to emulate and ultimately improve what humans can do. It also means having abilities and tools that do what humans cannot. There is a wide range of advantages that come from the practical utilization of AI and machine learning. Both are extremely helpful with streamlining intricate business operations. The following major benefits of AI and machine learning have been employed by companies for years:
- Improve and configure forecasting towards data-driven decisions
- Create, train, and deploy machine learning models
- Improve the efficiency of identifying people and objects in images
- Based on thousands of variables identify which have the biggest effect on your target, far faster and more comprehensively than any human can do
- Automated re-evaluation; with new data and a strong DevOps set up, retrain your models and create new predictions automatically
AI and Machine Learning in Financial Services
It has to be said that quantitative methods and algorithms were employed on trading floors way back in the 1990’s. This provided the basis for the creation and incorporation of high-tech AI systems. With the eruption of big data, machine learning experienced a massive jump in practical application, creating tremendous market opportunity. The automation of processes, powerful frameworks and governance guidelines are now the building blocks used by banks and various financial services firms
to manage increasingly complex data. With the employment of cloud-based tech, financial services can now access an array of impressive machine learning tools right off the bat.
Enhance Your Trading Strategy with 6 Cool Cloud-based Machine Learning Tools
These days it is widely acknowledged that algorithmic trading depends on utilizing the proper tooling. Both AWS and MS Azure have created rich and fruitful foundation for the evolution of machine learning. Amazon offerings denote pre-trained AI models – quite suitable for forecasting, recommendations, computer language and so on. But there’s more to it. It becomes easy to build better models faster if you are relying on the power of the cloud. Azure Machine Learning Compute, with tools such as Azure Databricks, and with advanced hyperparameter tuning services, allows you to automate model training and tuning using the SDK. The following tools are ideal for machine learning within the financial landscape:
Boasting solid machine leaning and deep learning capabilities, Azure Databricks is just the ticket. With it you’ll unlock Databricks Runtime for Machine Learning (Databricks Runtime ML), which kicks off an environment for machine learning and data science. You are essentially utilizing a handy and intuitive environment for building, training, and deploying ML and DL models at scale. Not to mention the benefits for data scientists – instead of worrying about infrastructure and maintenance, they can focus on their specialized work. Let’s face it, you want data scientists and data engineers working in reliable environments. The tool also enables you to pay only when you are processing/computing, hence it’s cost-effective.
Azure Batch AI
Financial services can benefit greatly from this particular tool. With Azure Batch AI, you can effectively process multiple forecast models. The cloud service helps data scientists and AI researchers train and test machine learning and AI models at scale within Azure. One of the great advantages of Azure Batch AI is parallelization, which is indeed the key in a variety of forecast scenarios. This, of course, denotes the ability to run operations concurrently. So, whatever operation needs to be completed, it can be divided into chunks, and distributed across multiple worker nodes in parallel streams. The result is numerous nodes working together efficiently towards the answer. Bear in mind though, that this may not apply to certain workloads because it depends on the underlying logic, code, data and cost/benefit.
How do you drive sales and maintain customer loyalty? Personalize is certainly an effective way to do it. Its function helps businesses gauge customers’ usage patterns. The same patterns are then utilized to create recommendations best suited for the business.
Speaking of trading, Forecast is a pretty cool way to use existing datasets to deliver time-series estimates for businesses. The cloud-based tool can predict a variety of key factors, including business overheads, and customer support. It can also efficiently forecast future stock prices.
With the aid of powerful AI and machine learning, Textract extracts info from scanned documents. The handy tool accurately identifies PDF tables, data and various forms and puts them into relevant context based on vital documents. This significantly reduces the time and resources needed to digitize documents and effectively eliminates manual work.
Amazon’s Fraud Detector
Definitely popular among diverse financial services firms, given that it helps mark risky or potentially fraudulent accounts. In Amazon Fraud Detector, businesses can enter their current data of flagged fraudulent transactions thus preparing and training the tool for future use. This helps mitigate and eventually eliminate further risks.
Fintech Machine Learning Stats
The utilization of AI has worried many businesses due to potential drawbacks. The unpredictable nature of AI behavior has often warded off a lot of businesses. That did not stop other investors and companies from focusing on the development and application of machine learning in fintech, and finances. According to recent statistics from satisfied investors
, sophisticated algorithms have handled their tasks pretty well. Not only that, but companies that use machine learning in businesses have also marked a rise in profits. Check out the stats below:
- 25% of banks invested into the development of AI systems to counteract fraud.
- 46% of fintech companies deem it vital it to invest in AI within the next 12 months.
- Banks are going to save $1 trillion by 2030 thanks to the use of AI.
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