Top 5 Immersive Applications of Artificial Intelligence in Finance
What if AI can mint money for you? That would be the most ideal scenario, right? That is exactly where the world is headed in the area of finance. We have often been fascinated by the world of artificial intelligence, decisions based on investments, saving money for larger financial institutions, and loan decisions need deeper insights that can be missed by the human mind. But AI can do the trick for us.
Finance is one field where AI is paying dividends. The financial world was the first sector to adopt AI in comparison to other sectors. Applications of AI and ML in finance are myriad. Applications of AI in finance range from mere chatbot assistants to fraud detection and task automation.
Billions of data points can be examined, patterns can be discovered, trends can be predicted, future patterns can be discovered, AI is buzzing. There are numerous applications of computer vision, natural language processing, deep learning in the field of finance which we will deep dive into in this blog.
Applications of AI in Finance
- AI in Personalized Banking
- AI in Credit Decisions
- AI in Risk Management
- AI in Trading and Investment
- AI in Fraud Prevention
AI in Personalized Banking
A 2019 Accenture Study on consumer patterns in finance outlines four banking consumer personas:
- The pioneer
- The pragmatist
- The skeptic
- The traditionalist
Out of four groups representing 47,000 banking and insurance customers globally, only traditionalists showed true resistance towards using personalized data to help improve customer experience. 80% of respondents said they would be willing to share their data in return for personalized services.
Personalization in finance include:
- Product Recommendation
- Personalized Virtual Assistant
- Personalized Investment Suggestions
- Personalized Product Compilation
Based on the historic data the machine learning application has the power to predict all of the above and provide customers with the best experience.
AI in Credit Decisions
A landmark 2018 study conducted at UC Berkeley found that even though fintech algorithms charge minority borrowers 40% less on average than face-to-face lenders, they still assign extra mortgage interest to borrowers who are members of protected classes.
Banks are deploying AI to make credit decisions thanks to the rapid increase in data availability and computing power. Moody’s Analytics RiskCalc model serves as a benchmark model for calculating risk.
Loan application processing is a time-consuming process, banks fail to respond in a short time and that indicates a lack of efficiency on their part. Customer deduplication, credit score, credit history, social life analysis, earning patterns, etc are to be clustered and studied for banks to make accurate lending decisions.
- Credit qualification
- Limit assessment
- Pricing optimization
- Fraud prevention
AI models check all of the above factors and improve credit-approval turnaround time, percentage of applications approved.
It is impossible for humans to do a deep analysis, especially analyze the depth of historic data to come to a conclusion. Hence AI is immensely helpful in this area.
AI in Risk Management
Derisking risk – that’s the power of AI. The recent financial crisis and COVID 19 kind of emergencies gave financial services firms a lot of problems with credit-challenged consumers. With AI-powered technologies, businesses are gaining access to big data about consumers’ behaviors and needs.
The emergence of alternative lending and online lending platforms has just added to the potential risk faced by banks. An insight from Capgemini revealed that “these non-traditional lenders use technology-based algorithms and software integrations to assess credit profiles of customers and are also leveraging alternative data such as social media photos and check-ins, GPS data, eCommerce and online purchases, mobile data and bill payments”
It is the need of the hour for banks to start using cognitive technologies to gain a competitive advantage and use risk to power their organization’s performance. It is predicted that risk managers of financial institutions will focus on analytics and stopping losses using the forecasting made by AI models.
AI in Trading and Investment
AI is shaping the future of stock trading drastically, it is making trading profitable. Robo-advisors are playing a key role in automating to analyze millions of data points in near-real-time and forecast the prices on the basis of that. AI can further execute trades at the most profitable time because of its ability to carry out several trades every second in the stock market.
AI in trading is used for
- Pattern formation
- Predictive Trading
- Increased Trading Speed
AI in Fraud Prevention
The power of AI is it can detect fraud attacks in a fraction of seconds using technologies like Omniscore. When a business relies on rules and logic the fraud attacks are very difficult to catch. In that scenario, it takes about 6-8 weeks after fraud has taken place, until then major damage is done.
AI-based fraud prevention system evaluates historic data and anomalies to stop attacks. With AI a fraud analyst gets a 360 degree view of transactions for the first time. They can instantly validate their decision regarding threshold levels, managing risk well.
Data Labeling for Creating accurate AI (NLP and CV) models to
Unstructured data that is collected to create AI models to improve the functioning of financial institutions is also called unlabeled data and cannot be used for training a certain kind of AI-oriented model.
The training data contains the communication within the humans on a particular topic and when they are annotated with Data Labeling the communications with humans becomes much easier.
There are multiple ways to get your training data annotated:
- Manual Data Labeling
- Crowdsourced Data Labeling
- Automated (Ai-powered) Data Labeling
Manual Data Annotation is not recommended because of the following reasons:
- Managing and maintaining the quality of data labeling
- Workforce management
- Keeping a track of the cost incurred
- Compliance with data privacy requirements
- The task to ensure data security
Crowdsourced Data Annotation has these challenges
- Managing data quality is difficult, most data labelers want to just finish the task and get their pay which does not negate the fact that your data might be mislabeled most of the time.
- Freelancers doing this task are typically unvetted people sitting at home and performing the task.
- Project Management gets daunting.
- The best way forward is adopting an Ai-powered data annotation tool that can eliminate all the challenges of the above two methods.
- Moreover, ai-powered data annotation tools are not expensive at all, they are a simple, reliable, and most efficient way to annotate your data.
Leverage The Smart Data Annotation Platform – Labellerr
The use cases are marvellous but one bottleneck remains. The high quality labelled dataset to train your deep learning models to adhere to any one of the above-mentioned use-cases.
Are you encountering a similar problem? Training a Computer vision model requires a voluminous amount of high quality labelled data. Which in turn needs a sophisticated enterprise-scale, cloud-based annotation system. Where you can plug your data stream from the cloud/ on-prem based databases and get labelled data with confidence scores for training the computer vision-enabled image segmentation models and accounting for a cent per cent data privacy.
If yes. I got your back. Let me introduce you to a one-stop solution to all your machine learning needs. Labellerr
Labellerr is a data-annotation platform that provides simple, clear and easy to use UI with seamless UX to perform annotation on different types of data catering to a wide array of industries, Retail, Health care, E-commerce, Hospitality, Businesses to name a few.
For your, AI needs Labellerr provides pre-trained models and products that can deliver Plug n-Play APIs with basic functionality to reach clients and can be modified on request easily and swiftly.
Leverage the auto-label feature on Labellerr to annotate your data with 10x speed and save crucial man-hours.
Follow along with this video to get an idea of how to perform segmentation annotation on Labellerr.
Automated ML experience on Labellerr
Experience the truly automated machine learning experience with Labellerr’s complete ML suite. Just plug in your data via a range of connectors as FTP, Local storage, Google Drive, AWS S3, Azure Blob etc.
Allow our inbuilt Auto ML feature to suggest annotations based on your requirement. Leverage the Auto-label feature to annotate your data with 10x speed and save crucial man-hours. Get a list of confidence scores of the assigned labels and verify only those with a low score.
Connect Labellerr to the Cloud-Based Compute service of your choice with assured data privacy and train your machine learning models on the go. Without the hassle of downloading the data, Service-specific data conversion formats, Data Leakage to name a few.
Labellerr’s community service initiative
As a part of our community service initiative, we have created a GitHub repository, wherein we list the implementations and walkthrough guides of tools, technologies, state of the art algorithms catering to the latest developments in the field of Deep Learning and do our bit in building a strong community of deep learning enthusiasts.
Head over to our blog where we regularly write about the industrial and corporate use-cases of Deep Learning. The recent advancements in the field and how the current industry is accepting them, building over them in pursuit of solutions that were once deemed unachievable.
Connect with us
Have any other use case in mind. Visit our website and mention your use case in brief and our customer engineer will contact you and help you prepare the plan and get you running on a trial with us to validate.