Artificial Intelligence in the Fintech Space

Click here to listen to our panel discussion with guest speakers from Ekata, Oiga Technologies, and ASI Group.

Artificial intelligence is gaining steam in the business world to attain a competitive advantage but what about in fintech? A recent study survey from a London VC firm, MMC, found that 40 percent of European startups purport to employ AI but do not actually use it in a “material” way to their business. The term AI is becoming the new hackneyed “disruptor” buzzword, similar to “core competency,” or “synergy.” So how does a company truly get a competitive advantage by using artificial intelligence, especially a fintech?

In addition to the many features Lead Envy offers, current initiatives include artificial intelligence and machine learning.

Artificial Intelligence helps fintech companies gain a competitive advantage.

Machine Learning

First, let’s explore Machine Learning (ML). ML is a branch of the artificial intelligence trunk that utilizes the study of computer algorithms to allow programs to improve with experience. Employing ML concepts is one way to achieve AI in business practice. The key is that systems improve without explicitly being programmed, allowing the programs to learn from data themselves.

The top 10 machine learning languages, in no particular order, are:

  • Python
  • Java
  • C++
  • JavaScript
  • C#
  • Julia
  • Shell
  • R
  • TypeScript
  • Scala

Some examples of real-life machine learning systems are Siri, Alexa and Google Now. Sometimes, they will even ask, “Was that helpful?” or “Did that answer your question?” They use the answers to learn for the next time they visit the topic, as an example.

Machine Learning is a branch of artificial intelligence – machines improving themselves.

Different Types of Machine Learning

The three main types of ML are 1) Supervised Learning, 2) Unsupervised Learning, and 3) Reinforcement Learning.

Supervised Learning

Supervised learning is done with prior knowledge of the output values. It describes a class of problem that involves using a model to learn a mapping between input examples and the target variable. The goal is to learn a function that, given a sample of data and outputs, approximates the relationship between input and output observed.

Common algorithms in supervised learning include logistic regression, naive bayes, support vector machines, artificial neural networks, and random forests. 

The two main types of supervised learning problems are classification and regression.

  • classification – supervised learning problem that involves predicting a class label
  • regression – supervised learning problem that involves predicting a numerical label

Unsupervised Learning

Unsupervised learning differs from supervised in that it uses a model to describe, or extract, relationships in the data. The core difference is that it operates only on the input data, without outputs or target variables.

The two main types of unsupervised learning problems are clustering and density estimation.

  • clustering – unsupervised learning problem that involves finding groups in data
  • density estimation – unsupervised learning problem that involves summarizing the distribution of data

Reinforcement Learning

Reinforcement learning describes a class of problems where the system operates in a confined environment and must learn to operate using feedback.

Upcoming Webinar on Artificial Intelligence and Fintech

An upcoming Lead Envy webinar taking place on September 10, 2020 will feature speakers from Oiga TechnologiesStickboy, and Ekata.

In future posts, we will explore specific solutions for online lenders and similar companies in the fintech space.

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