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What’s the Difference Between Predictive Analytics & AI?
Predictive Analytics

What’s the Difference Between Predictive Analytics & AI?

In recent years, AI has transformed from a concept only heard of in Si-Fi movies to an everyday business reality. Its solutions are employed across all types of companies, from high-tech giants to supermarkets to insurance brokers, and everything in between. Big data and predictive analytics software are also trending, with 90% of companies stating that their business depends on data analysis and that they plan to increase their budget in this area. 

Both AI and predictive analytics are two terms frequently heard together. But what do they mean precisely, and what’s the connection between them? And more importantly, how can you harness the power of AI and predictive analytics for financial software in your business. Let’s find out. 

What is artificial intelligence?

Artificial intelligence, more commonly known by its abbreviated form of AI, is a type of computer science that attempts to emulate human thinking. Does this mean that machines think for themselves? Well, yes and no. AI uses an algorithm to ‘think,’ meaning it makes its ‘decisions’ based on the information it is programmed with or programmed to learn. AI is capable of learning from data. This is a concept known as Machine Learning. Some of the most well-known AI solutions include Google Search, smart assistants such as Siri or Alexa, stock trading robo-advisors, and social media platforms like Facebook.

What are predictive analytics solutions?

Predictive analytics is a type of technology that studies historical data and external data and utilizes them to draw conclusions about the future—a kind of “what if?” scenario. Unlike descriptive analytics, which defines current events, predictive analytics goes further to surmise what these might mean going forward. Such predictive analytics solutions are employed in numerous industries, from stock trading to deciding the right insurance rate for a client.  

How are predictive analytics solutions and AI different?

As we’ve seen above, predictive analytics and AI are separate concepts and tools, but ones that are closely connected. While the technologies are independent of each other, in real-life usage, it’s often not a matter of “if” or “which one?” Instead, it’s all about how to choose the optimum combination for your business needs. Here’s how it can work. 

How can you utilize predictive analytics and artificial intelligence in your business?

It’s one thing having data at your fingertips. It’s another to be able to use it purposefully. Let’s take a look at the process of using data in decision making software. For our example, we look at loan scoring. However, such solutions can be employed in almost every business area that requires data to make decisions, from knowing your customer base to adapting your business offerings, to risk management and more.

Loan scoring using predictive analytics and AI

Extract. The first thing you need to do is select and extract the data you need to perform the analysis. For example, for loans, you may choose to combine all the loan data for a particular grouping with an individual’s credit history.

Prepare. At this point, it’s time to refine the data and prepare it for usage, which data is genuinely relevant to your situation. For example, in loan scoring, you may find it helpful to refine using, for instance, by profession. However, you may wish to narrow it down to a particular state or area to give a more precise overview of earnings. 

Choose. Here you decide precisely what you want to predict. Will you use this data to give you a better understanding of your next marketing campaign or to predict the likelihood that your client will repay their loan? Or something else entirely?

Analyze. Using artificial intelligence scoring technology, your process is now underway. At this stage, all that data is being analyzed using AI to deliver you the results you require. AI allows you to analyze big data based on the latest information. 

Predict and Act. With your predictive data in hand, in an easy-to-understand format, you can now take action and give that client the long-awaited yes or no answer to their loan application. Using machine-learning credit scoring, you can optimize the loans you offer and correlate them to client risk, empowering your business with predictive analytics and AI.

Alternatively, you can opt for a tailored solution, such as GiniMachine, which does all your complicated data work for you. Gini Machine utilizes data to help your company avoid bad loans and other poor financial decisions. 

Why do you need AI or predictive analytics for credit scoring and other financial software?

Now that you know how the technology works, why should you use it, and is it really worthwhile? These are the three reasons that you should consider adding predictive analytics and AI to your business, especially if you work in the financial sector. 

Base decisions on real-life data

The business world is moving fast. Last year proved once again that some events can have a significant impact on the future of your business, and this is difficult to plan for. By using the latest data, you can make decisions that impact your business, such as up-to-date credit scoring, to ensure that you do not place yourself under unnecessary risk when presented with changing circumstances. 

Avoid bad loans and poor decision-making

Past advice of putting on a slick suit and heading to the bank to get a loan has long become a distant memory. In its place is smarter decision-making. Utilizing a complex variety of data, AI, and machine learning credit scoring tools, reducing bad loans by up to 50% is already possible today. Imagine the power technology has to influence your business decision-making to make smarter choices. 

Plan to perfection

But AI and predictive analytics aren’t all about to prevent the worst. It’s about future planning. Knowing the current risks, current market situation, and existing customer data allow your business to plan for the future more efficiently. Which direction will you take your business in? Who will your target audience be? And are the decisions you’re making today applicable for the future? 

Predictive analytics and AI – the future

Currently, the technology behind predictive analytics and AI in the financial sphere is only starting to emerge. But that doesn’t mean it’s experimental. Instead, it’s essential for all businesses that need to analyze and work with a huge variety of data. And this is all businesses. In order to stay relevant, make smart decisions and drive your business forward, you need to start looking at technological solutions today.

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