The Future of Finance Depends on AI and Unstructured Data – Here’s Why
AI and unstructured data go hand-in-hand because AI is uniquely suited to help us make sense of unstructured data. Unstructured data is information that doesn’t have a predefined structure or format, making it difficult for traditional software programs to process and extract value from it. This includes things like text, images, audio, and video.
On the other hand, AI excels at dealing with unstructured data. Thanks to machine learning, AI can learn to recognize patterns in data that humans would never be able to discern. This ability gives AI a huge advantage when it comes to making decisions based on unstructured data.
That’s why the future of finance depends on AI and unstructured data. With AI, we can make better decisions about where to invest our money and how to protect ourselves from financial risks. We can also use AI to automate repetitive tasks in finance, freeing up human employees to focus on more important work.
Let’s take a closer look at unstructured data and how AI can help us make sense of it.
What is Unstructured Data?
Unstructured data is any information that doesn’t fit into a pre-determined data model. This can include text, images, audio, video, and more. While this type of data is messier and harder to work with, it’s also where the real value lies. In a world where businesses are generating more and more data, the ability to make sense of unstructured data is becoming increasingly important.
What is Structured Data?
Structured data, on the other hand, is information that can be easily sorted and organized. This includes things like numbers, dates, and addresses. While structured data is easier to work with, it often doesn’t tell the whole story. In order to get a complete picture of what’s going on, you need both structured and unstructured data.
Examples of Structured and Unstructured Data
Here are some examples of structured and unstructured data:
Structured Data:
- Numbers
- Dates
- Addresses
Unstructured Data:
- Emails
- Social media posts
- Images
- Videos
- Customer reviews
As you can see, unstructured data is often more difficult to work with. However, it can also provide a more complete picture of what’s going on. That’s why the future of finance depends on AI and unstructured data.
AI can help us make sense of all this unstructured data and use it to make better decisions. For example, by analyzing social media posts, AI can help us identify potential fraudsters before they have a chance to commit any crimes. And by analyzing customer reviews, AI can help us identify areas where we need to improve our products and services.
How is Unstructured Data Analyzed?
In order to make sense of unstructured data, businesses need to invest in AI and machine learning technologies. These technologies can help businesses automate the process of data analysis, making it easier and faster to glean insights from large data sets.
The methods are largely statistical in nature. The algorithms seek for patterns or relationships between various records. Are the same words frequently found in the same sentence or paragraph? Is there a relationship between two values of a sensor on a particular day? Are certain colors prevalent in an image?
Modern algorithms, in general, add a basic layer of organization to the data source, which is known as embedding the data or constructing an embedding. A text, for example, might be searched for the 10,000 most frequent words that aren’t used elsewhere. An image may be broken down into sections. This loose structure serves as a basis for further analysis.
Numeric data, such as financial reports, can be more easily organized into a standard format because it is already in a structured form. However, unstructured data, such as images and text, needs to be converted into a numerical representation before it can be analyzed by machine learning algorithms.
This process is often as much an art as it is a science. The majority of data scientists’ work involves devising and testing various approaches to building the rough embedding.
In many situations, domain knowledge may allow a person to move their understanding from the subject to the algorithm. For example, a doctor might decide that all blood pressure readings above a certain value should be classified as “high.” An insurance adjuster could determine that all rear-end accidents are caused by the vehicle behind. These rules provide structure to the embedding and can be used to improve performance.
In contrast, unstructured data generally doesn’t have any pre-determined rules or organization. Audio recordings, natural language text, and video are all examples of unstructured data. Unstructured data is more difficult to analyze because there isn’t an easy way to apply structure to it.
However, unstructured data is often more rich and informative than structured data. For example, a doctor might be able to glean important information from a patient’s body language that wouldn’t be captured by a blood pressure reading. An insurance adjuster might be able to spot patterns in accident reports that would be missed if they only looked at data about the location, time, and date of the accidents.
Organizations are only just beginning to scratch the surface of what they can do with unstructured data. The future of finance depends on our ability to harness the power of unstructured data. With the help of artificial intelligence, we will be able to make better decisions, faster than ever before.
So if you’re in the financial sector, don’t forget to keep an eye on AI and unstructured data. They just might be the key to your success in the future.
