Data Science is the new frontier for many domains, and Finance is no exception. In this day and age, cognitive technologies are able to cope with a wide array of financial tasks. If you want to learn how to apply AI advancements to reshape your business, this article will walk you through the areas where adding AI can bring the most value.
Data Science can enhance many business elements, but more importantly, it can give birth to a new business model and facilitate astounding growth. Let’s dive in!
A vital business operation, risk management helps companies identify potential risks and mitigate their impact. Risk management has undergone significant changes over the year, giving birth to a whole new field – AI-induced risk management, where machine learning models are under the hood of business development.
Risks refer to uncertainties in the market that can bring on threats due to failure, legal liabilities, accidents, regulators, the list goes on. Businesses can identify them early on and monitor activities to spot potential risks. That’s where Machine Learning is fit for purpose. Feeding a vast amount of data, data scientists build predictive models that largely improve risk scoring.
Case in point, determining the creditworthiness of potential customers. Machine learning can be applied to a wide scope of operations, from digging into their spending behavior and credit history to choosing a suitable credit product.
An example of such is Equifax, a big player in the field of consumer credit reporting. In an interview this year, Peter Maynard, Senior Vice President of Global Analytics at Equifax, claimed their new “neural network improved the predictive ability of the model by up to 15 percent.” Using it to look back at recent decisions, they found that loans that were turned down could have been made safely.
Automating risk management has huge potential, even though it’s still a nascent category. To make the most of it in a Finance organization, its leaders should add analytical skills and introduce relevant technology solutions.
Data is the biggest asset of Finance organizations. If you manage it correctly, your profits will grow. Financial data sets come in different sizes and structures. It may include data from social media, transactions, historical data, and more. Even now, the lion’s share of finance specialists has to wade through tons of information without algorithms’ help.
Without a doubt, many organizations could use the power of machine learning to glean insights from data that allow making data-driven decisions. Moreover, companies can reduce the number of routine tasks for their employees, like reporting by implementing AI technologies.
For example, JPMorgan Chase recently introduced a Contract Intelligence (COiN) platform designed to “analyze legal documents and extract important data points and clauses”. This machine learning technology allows analyzing thousands of commercial agreements in seconds and this means a dramatic reduction of the time spent on back-end processes.
Finance organizations should maintain the utmost level of security. However, finding a solution that would ward off cybercriminals with a 100 percent guarantee can be problematic. Employing AI models calls for skillful data scientists who will be responsible for crafting anomaly detection systems to automate anti-fraud processes. A model can be trained to detect unusual transactions or activity and block them. Although building such models can be time-consuming, the effectiveness is beyond any doubt. An example of such is below.
Danske Bank modernized its fraud detection process, which reduced its purported 1,200 false positives per day. By the time Danske Bank had finished installing and implementing the AI solution, they were able to:
AI drives personalization in Finance organizations, changing customer experience for the better and helping companies outstrip competitors in the war for customers. AI can help you offer personalized services to your customers based on their past purchase behavior and personal preferences.
An AI tool, like natural language processing, takes personalization to a whole new level. AI-powered chatbots that guide customers to the right solution to the problem will take the load off call centers.
Thus, implementing AI and machine learning models removes the heavy lifting, such as scheduling payments, tracking funds movement, suggesting personalized offers, and optimizing spendings.
Case in point, JPMorgan Chase. The company invested in technology and introduced a Contract Intelligence (COiN) “chatbot” designed to “analyze legal documents and extract important data points and clauses” in 2017. Manual review of 12,000 annual commercial credit agreements normally requires approximately 360,000 hours. Results from an initial implementation of this machine learning technology showed that the same amount of agreements could be reviewed in seconds.
One of the biggest beneficiaries of AI and predictive analytics is trading, an area where everything can change in a matter of seconds. In fact, the advent of AI has given birth to a new branch in trading, such as high-frequency trading.
Data Scientists create models basing on recent data. That enables traders to make real-time data-driven decisions. In trading, whoever makes the right decision first stays ahead of the curve. Thus, processing new data over a short time gives the traders the edge.
Putting together real-time and predictive models can make predictions for market opportunities more accurate. AI processes information that comes from diverse sources – news, social media, books. That allows them to understand current events and their effect on the financial markets.
An example of AI implementation in high-frequency trading is our customer, FRST, a Blockchain analytics and strategy company. The platform offers a set of sophisticated datasets and tools designed for data scientists, developers, investors, and traders to continuously test, deploy, and share strategies. Our developers from NCube are contributing to building a system where the information about blockchain is collected in the shortest possible terms.
AI fuels innovation in the Finance sector and its benefits are undeniable. From reducing manual, pen-and-paper work to processing ever-changing information and enabling fast and proper data-driven decisions. Overall, Finance and AI are a perfect match, given that it generates tons of financial data that begs to be analyzed and processed quickly. Doing so, financial organizations can be fully data-driven.
This guide is an overview of the mega-popular technology, that is Artificial Intelligence. It includes key concepts, use cases, and risks of joining the AI movement.
To dive deeper, let’s define AI. From a business standpoint, Artificial Intelligence (AI) refers to using intelligent machines or systems to solve business problems. The machines can be trained to perform tasks by processing a massive volume of data to identify patterns.
СTO Vision names several elements that are the enablers of a good AI case. They are divided into two groups: technology and business.
Technology side
Business side
According to the IBM report, some areas that benefit from AI implementations. We can find good AI use cases in all industries. But IBM singles out several domains where AI has a great potential.
Other points to consider:
AI is a viable solution to many problems. Its benefit is achieved in the three domains we described previously. Now let’s have a look at the successful AI use cases from IBM.
Production of goods: a neural network detects aberrations in the work of machines. The network is trained using data received from sensors placed on the truck engines to identify the deviations from the norm.
Automotive industry: predictive error detection for welding robots and predictive maintenance assessment. Supervised learning techniques are used to develop predictive models to serve as early warning systems for failures. They continuously receive system messages. This way, the manufacturers can do maintenance work when needed to reduce the downtime, false alarms, and unnecessary workload.
Energy suppliers: A microgrid forecast of energy demand and production optimization. Together with predictive mathematical optimization, predictive ML models show which the power sources will be the most cost-effective.
Raw material supplier: An informative dashboard. Together with the customer’s procurement experts, IBM analyzed the business dynamics and made a list of potentially relevant data sources. Several models for machine learning were trained to learn price fluctuations and predict future price changes.
While implementing AI can change your business model, there are many risks to it.
Here are the main AI technologies your company can benefit from, according to CIO.
Machine learning
This subset of AI is the foundation of any organization’s AI strategies. Indeed, it’s proven to bring a lot of value, especially in complex solutions. The solution includes algorithms, development tools, APIs, and model deployment, and many more. Machines are taught without explicit programming.
Deep learning
Another approach to data resembles how the brain works and uses artificial neural networks. The information is flowing through these networks, altering their structure. As such, they can change (and be “trained”) depending on the input and output. The learning is based on observing the data sets. Deep learning is a growing trend that fits in well with Big Data. In particular, it presents value to pattern recognition and classification.
Natural language processing
This technology has to do with the interaction between humans and machines. Computers can analyze and understand spoken and written language. Voice assistants such as Alexa, Siri, and Google Assistant all use NLP to handle voice requests. NLP is broadly used in customer service and support as chatbots and voice assistants, but can also bring a lot of value to an organization’s internal processes.
Natural language generation
The software-based on this technology turns all kinds of data into readable text. This technology can automate business analytics, product description, finance reporting, all sorts of reminders, and more. All in all, it lets you create content instantly. Structured data can turn into text with high accuracy and at a record speed of several pages per second. Companies like Automated Insights, Lucidworks, Attivo are worth watching.
Virtual agents
To many people, the difference between a virtual agent, a virtual assistant and intelligent virtual assistant is merely semantics. Some try to distinguish between them and regard virtual agents as customer assistants, while they are more akin to online assistants in reality. Virtual agents are often known as AI-induced bots that can lead a meaningful conversation with a user. One advantage is that the clients can be assisted at any time. But they also have a great potential in other fields such as healthcare and heavy industry.
AI hardware
These include devices with embedded AI, microchips, and GPUs. The combination of hardware and AI goes beyond consumer devices such as games and entertainment. Hardware with embedded AI will be used to advance deep learning. Key players to watch are Google, IBM, Intel, Nvidia.
Robotics
Robots that are controlled by AI programs and are used to automate manual work. This technology is mainly used at warehouses and factories now but showing great potential in medicine as well as business and smart homes.
Robotic Process Automation
RPA automates business processes intending to redistribute the human resources to perform more complex tasks that bring more added value. It is a technology that allows organizations to configure software (software robots) to perform repetitive, mechanical operations at the user interface level. Software work performs tasks in the same way that people do, receiving, sorting, processing data, and performing specific actions with them, without changing the organization’s IT landscape.
Supervised Learning. One of the machine learning methods, where data labeled by humans is fed to a machine so it can be taught. A ready model is used to provide predictions from new data.
Unsupervised Learning: Opposed to Supervised Learning, models of this type detect patterns in unlabelled data and where outcomes are unknown.
Training Data: An initial set of data used to develop a model. It helps a program understand how to apply technologies like neural networks to learn and produce results.
If you want to successfully integrate AI into your business, partner with NCube. We will build a team of highly-skilled data scientists and machine learning engineers to provide you a solution that addresses your business needs. Be sure to contact our team via the form below for more information on building your virtual tech team in Ukraine. Meanwhile, please take a look at our success stories, including our contribution to AI projects.
