Predictive analytics 101: what and why it matters
It’s a fact – predictive analytics is the only way to make sense of the endless amounts of data that financial institutions have generated. It is what allows you to turn raw bits of information into key insights and actionable solutions and it plays an indispensable role in almost every industry – particularly traditional banks and insurance companies who have access to rich and comprehensive amounts of data.
On the surface level, it seems self-explanatory. CIO defines predictive analytics as the process of analysing past data to predict future outcomes. This sounds simple enough but as AI technology continues its rapid advance, the importance of analytics is only increasing and this warrants an in-depth look at how predictive analytics differs from the other types of analytics, how it synergises with AI, and why it all matters.
What are the types of data analytics?
Predictive analytics has two siblings—descriptive and prescriptive analytics. But despite being the middle child, it is the most popular and most discussed. Its siblings however, are no less important; in fact, all three rely on each other.
Descriptive analytics is the most basic form of analytics. It looks back at the past and asks, “What happened, why did it happen and how did it happen?”
It drills down into the data to reveal details and causes. Without doing this, past data is largely useless. Data aggregation, mining, and statistical methods are the tools in play here.
A simple example is a report showing a bank its average revenue per customer by for a certain loan product.
Predictive analytics studies the data and asks, “What is going to happen, why is it going to happen, and how is it going to happen?”.
It is all about estimating probabilities for certain future outcomes to create actionable insights. It does so by using statistical models and machine learning tools to better understand the relationships between different data sets.
An example almost everybody would be familiar with is the humble credit score, which is nothing but a number corresponding to the assigned probability of a person’s capability to repay any debt.
Prescriptive analytics is less known, it tries to answer the question that naturally follows, , “What should we do about it?”.
It attempts to investigate and quantify the effects of the varying actions that could be taken in response to the insights gleaned from the descriptive and predictive analytics processes. Instead of prediction though, it is about optimising decision-making. Advanced tools including simulation and modelling are necessary to carry out prescriptive analytics properly.
Effective customer targeting is one example. For instance, a bank may use prescriptive analytics to determine which customers will be most likely to place higher assets under its management.
How is predictive analytics related to AI?
Predictive analytics is underpinned by AI technologies such as machine learning and neural networks.
In the past, companies wishing to adopt predictive analytics faced the challenge of accessibility. Unless they were willing to employ highly trained data scientists—and bear the associated costs—it was nearly impossible for them to reliably integrate it into their businesses. Now, thanks to a proliferation of prebuilt, off-the-shelf AI solutions in the market, accessibility to AI has vastly increased. This trend is only likely to continue. Predictive analytics has never been more accessible.
But while this has allowed companies to reap some of the benefits of predictive analytics, they are still far from unlocking its full potential. The reason? They are lacking the skill and insight that trained data scientists with domain-specific knowledge can bring. Sure, data scientists might have been involved in the creation of these off-the-shelf AI solutions, but they are far removed from the day-to-day operations of the companies who are using these solutions. Thus, these companies remain unable to fully realise the capabilities of predictive analytics.
This is what creates the AiDA Advantage. We don’t ‘sell and forget’ about our solutions, we back it up with a niche team of highly trained data scientists that continually tweak and update our prediction models and algorithms to better fit our client’s specific business needs. This leads to more accurate predictions and actionable insights.
We also integrate our solutions into our clients’ end-to-end processes—they are not an isolated tool. This is what generates maximum value and the highest ROI. It also takes AI decisions out of the ‘black box’ and makes them transparent. The financial services industry’s decisions affect far too many lives for them to be opaque. A major part of the AiDA Advantage is just this—high-quality decision making processes that are also explainable.
What is the role of predictive analytics in the modern big data economy?
Although AI’s ability to scale predictive analytics capabilities will make it more widespread, we are still only in the beginning. Several stats show that we are only in the early stages of the big data economy.
- In 2012, IDCs Digital Universe Study stated that only 0.5% of data generated is analysed
- In 2013, IBM calculated that 90% of the world’s data had been created in the past two years
- In 2016, Forrester Research estimated that 60% to 73% of a company’s data remains unused for analytics
While these statistics are not the most recent, simply extrapolating from the above trends indicates that the role of predictive analytics in the modern economy and this will only continue to grow. Here are just a few of the ways it is being used in the financial services industry as well as the solutions we offer in each area:
- Claims processing: Spotting anomalous patterns in customer behaviour to approve/reject insurance claims and detect fraud (AiDA Smart Claims and Smart Risk)
- Credit risk modelling: Assessing loan eligibility and credit worthiness (AiDA Smart Lending and Smart Risk).
- Customer engagement and marketing: Identifying potential high-value clients for targeted upselling and cross-selling (AiDA Smart Engagement and Smart Agency)
Is predictive analytics and AI necessary to maintain competitiveness?
The facts about the current big data economy, the importance of predictive analytics, and how AI has strengthened its capabilities all point toward a singular conclusion. Today, predictive analytics and AI can provide companies with a strong competitive advantage. But in the near future, they will be necessary merely to stay competitive.
Already, the advances and growth in the fintech sector has created pressure on the large financial services sector to keep up. To add to that, we are also witnessing the entrance of large tech players into the financial space such as Alibaba with Ant Financial and, more recently, Grab with GrabFinance. These tech giants also have the same advantage that the financial services players have—access to large amounts of data.
These trends mean that the financial services industry will only get more competitive, with competitive advantages centred around who can utilise the power of big data to the fullest. Banks and insurance companies who are not willing to invest in technology to make sense of that data risk being left behind.
At AiDA Technologies, we specialise in supercharging the financial services industry by providing a suite of multi award-winning smart technologies that drive results across all departments. We offer a unique combination of niche Ph.D. level technical talent, best of breed AI and machine learning technology, financial services and insurance (FSI) expertise, and customer-centricity that accelerates lending and claims processes, identifies revenue potential, drives cost reductions, and anticipates evolving risk – that is the AiDA advantage.
Contact us for a free trial using your company’s historical data to provide AiDA’s capabilities.