Machine learning (ML) is a modern innovation that has helped man improve not only many industrial and professional processes, but also promotes everyday life. But what is machine learning? It is a subset of artificial intelligence, which focuses on the use of statistical techniques to build intelligent computer systems in order to learn from the available databases. Currently, machine learning has been used in multiple fields and industries. For example, medical diagnosis, image processing, prediction, classification, learning association, regression, etc. Intelligent systems based on machine learning algorithms have the ability to learn from past experiences or historical data. Machine learning applications provide results based on past experience.
Machine learning has had many fruitful applications in finance long before the advent of mobile banking apps, competent chatbots or search engines. Given the high volume, accurate historical records, and quantitative nature of the financial world, few industries are better suited to artificial intelligence. There are more use cases for machine learning in finance than ever before, a trend that continues with more accessible computing power and machine learning tools.
Now with the tech culture of gathering and storing excesses of data, many more industries are setting themselves up to take advantage of Machine Learning as it can be applied to many other sets of data that have documented their sets of input and output combinations of variables and metrics.
Machine learning is set up to play an integral role in many phases of the financial ecosystem, marketing, and sales performance. However, few professionals with technical knowledge have an accurate view of how many ways machine learning finds its way into your daily financial life.
Classification Problems
Examples such as identifying credit worthiness, understanding who buys products for a company and determining good traffic from bad can all be generalized to classification problems, which a subset of Machine Learning applications.
Classification is the process of determining what category a set of observations belong. The strategy Machine Learning approaches is using a set of observations with known outcomes to build a Mathematical model that can reliably reproduce the expected outcomes with the known input and output pairs.
This process of Machine Learning happens by using the observations and known outcomes to allow an algorithm to optimize its underlying mathematical model. By using the known data, the algorithm over time becomes a specialist for this set of data and then uses the knowledge it built to classify new observations with unknown outcomes.
Some applications are outlined below:
Subscription of Loans / Insurance / Services
Subscription could be described as a perfect job for machine learning in finance, and in fact there is a great concern in the industry that machines will replace a large part of existing subscription positions today.
Especially in large companies (large banks and publicly traded insurance companies), machine learning algorithms can be trained in millions of examples of consumer data age, work, marital status and financial loans or insurance results, as if a person failed or not. or repaid your loans on time.
The underlying trends that can be evaluated with algorithms and analyzed continuously to detect trends that could influence loans and guarantee in the future (do more and more young people in a given state have car accidents? Are there increasing rates of default among a certain demographic population in the last 15 years)?
These results have tremendous tangible performance for companies, but today they are reserved primarily for larger companies with the resources to hire data scientists and the huge volumes of past and present data to train their algorithms.
Marketing and User Analysis
Similar to the application of ML to loans and credit worthiness, applying Machine Learning classification methodologies towards understanding trends of demographics, genders, and buying habits using consumer data.
Companies like Amazon and Walmart are already doing this with user data, but its is something that can be done by any size companies that are already capturing this data with their e-commerce orders.
Using sets of data like gender, age, salary, and sales (among other data points) a model can be derived to determine patterns of people that are likely to Buy (Category A) or Not Buy (Category B). Since historical data has already been gathered from past purchases, this can be utilized to build targeting marketing strategies or targeted sales strategies focusing on people more likely to buy specific products.
Sales Performance
Another area that can utilize the generalization characteristics of classification is in analyzing and interpreting B2B sales data. Data for improving lead generation, matching deals to customers, maximizing value to the customer, and optimizing sales to the market and for the customer are already hiding in dormant data. A wealth of knowledge can be generated using Machine Learning (ML).
An example set of variables that are invaluable for determining these metrics are previous products, customer retention, company size, business industries, and economic strength indicators. Utilizing a combination of these variables, a ideal match can be made that makes as close to perfect mate of seller and consumer.
By utilizing ML business can make better decisions that maximize revenue and time. Targeting specific customers and being able to advertise to just the right set of eyes can save money and also let sellers focus more specifically what their niche is.
Wrap Up
Classification is only one type of problem that Machine Learning is good at. Big Data is a pop-term that is floating around, but overall technology hasn’t scratched the surface of making annoying meaningful out of it. Many have coined the 2010 decade as the Age of Big Data. Petabytes of knowledge
It hasn’t been until the last decade that computers have had the computation power to reasonably run these models and produce good results. There is a wealth of knowledge that is hiding inside historical data, accounting software, and traits of industry leaders.
By using a combination of datasets and known outcomes, ML can optimize specific knowledge from data and generate models that produce consistent and correct results. To an onlooker it may not be apparent how the model works, but by using proofs against we already know it is easy to empirically evaluate its correctness.
MaddLogic LLC is a company that specializes in ML and Neural Network solutions for small to medium size businesses.
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