In today’s technology driven world where none of the activity of our day to day life is being untouched by technology, we finance people also need to realign our knowledge and experience with the trending technology to get a better pace and ease in surviving in this new era. We all must have heard about terms like Artificial intelligence (AI), Machine Learning (MI), deep learning etc. but most of us are still not much bothered about this or even few of us still have a thought in our mind that all these AI, ML are the engineer’s stuffs and we being a finance guy (CA, CFA,…..etc.) are least affected by these. So in this article I will be trying to make you aware about role of these new terms (for some of us) in our finance domain, pros and cons on our jobs and how it is so imperative for us to have sound skillset on these technology (AI, machine learning etc.)

 The science of today is the technology of tomorrow.    –  Edward teller 

The science of today is the technology of tomorrow - Edward teller

  Understanding few terminology

  • Artificial intelligence (AI): Artificial Intelligence (AI) involves using computers to do things that traditionally required human intelligence. This means creating algorithms to classify, analyze, and draw predictions from data. It also involves acting on data, learning from new data, and improving over time. Just like a tiny human child growing up into a (sometimes) smarter human adult. And like humans, AI is not perfect. Yet. Few example of uses of AI at present are: –

–  Facebook uses AI to recognize our face when we upload photos to FB, it puts a box around the faces in the photo and suggest friend’s names to tag. (I assume you all have used this feature, if not try it now)

– Customer support, Remember the online text chat we had with our banks, Mutual fund broker, insurance company. That may have been a chatbot instead of an actual human.

Artificial intelligence (AI)

So in simple terms, AI works in combinations of Data and technology to provide a meaningful decision.

  • Machine Learning (ML); Machine learning algorithms identify patterns and/or predict outcomes. Many organizations sit on huge data sets related to customers, business operations, or financials. Human analysts have limited time and brainpower to process and analyze this data. Therefore Machine learning can be used to: Predict outcomes given input data (Eg; Algo trading), Analyzing certain patterns in large data sets. Etc. Few example of uses of ML at present are:

– Netflix applies machine learning to your viewing history to personalize the movie TV show recommendations you see.

– Email spam and malware filtering in our regular mail box, Google and other search engines also uses ML to improve our search results. 

Machine Learing

Looking at the diagram above it seems quite confusing and let’s keep this task of understanding algorithms and programming to engineers only (on a lighter note) and don’t get deeper into these.

Role of AI, ML in finance domain:

Machine learing use cases in finance

There are various application of AL, ML in finance role in current scenario. Few of them are enumerated below:

Source: towards data science 

> Process Automation: Process automation is one of the most common applications of machine learning in finance. The technology allows to replace manual work, automate many repetitive tasks, and increase productivity in the workplace. This results into enables companies to optimize costs, scale up services and improve customer experiences.

> Security: With the growing no. of transactions, stakeholders and third party integrations, machine learning algorithms are doing a fantastic job in detecting frauds. So it indeed of a great help to any finance guy doing forensic audits, stress testing or analysisng bulk data to get desired outcome in lesser time and with more reliability.

AdyenPayoneerPaypalStripe, and Skrill are some notable fintech companies that invest heavily in security machine learning.

> Underwriting and credit scoring: Machine learning algorithms fit perfectly with the underwriting tasksthat are so common in finance and insurance.

Data scientists train models on thousands of customer profiles with hundreds of data entries for each customer. A well-trained system can then perform the same underwriting and credit-scoring tasks in the real-life environments. Such scoring tools help human employees work much faster and more accurately.

> Algo Trading: In algorithm trading, machine learning helps in better trading decision. Various mathematical models monitor news and other results on real time basis and analyse various patterns which can force prices to go up and down, and accordingly it acts proactively to sell, buy, and hold based on its prediction.

> Robo Advisory: Robo advisors are very common in finance domain now a days. They use machine learning in allocating Portfolio (portfolio management) of clients across investment opportunities using various algorithms and statistics.

Fraud detection: Machine learning has a significant role in in detecting fraud by way of detecting unique activities or behavior, and flag the risks which were never risk at first place. So being an auditor or financial/data analyst it become very robust system and provide fruitful results. 

Below are few of the major Pros and Cons of Artificial intelligence. You can have a look on it.

pros and cons of artifical intelligence

Future Value of Machine Learning in Finance

Some of us who work in banking or finance industry may find it difficult to convince our team members or upper management to consider an AI solution to a current problem within their business. It is important to note that while AI is beginning to create waves of disruption across these industries, not all companies may have the insight required to consider how AI might help them in the future.

Some experts, such as Ian Wilson, former head of AI at HSBC, consider this lack of information to be an important challenge to overcome when trying to find the right AI solution. He considers education and innovation to be the most effective ways of disseminating this information to other members of one’s business.

Innovation becomes a structure through which we can start developing ideas. A lot of people within each business line may have an idea, but because they’re not technical they have no way of knowing how to get the ball rolling to even test that out and see whether it’s feasible. So I think it’s also important to have that structure in place … so if you have an idea you can hand it over to a team that knows how to pick it up and test it out.”

So the application of AI on few below areas are looking promising, some are relatively active application today and others are still relatively nascent.

  • Customer service like chat bot etc.
  • New security 2.0 like Voice recognition, face recognition etc.
  • Recommendations/sales of various products like Amazon and Netflix recommend books, movies etc.

Conclusion

All being said, the pros and cons of artificial intelligence being evaluated, it is up to the reader, user, and their perspective. AI and robotics will improve the way we think, the way we explore new horizons, whether space or the ocean. As the age-old saying goes, “necessity is the mother of all innovations,” so is the case with AI. Human beings know what they need and are getting increasingly better in defining their wants and quickly transforming this into reality. In the near future, things will happen so rapidly that we will see major changes and innovation. So being a finance person we should also upgrade ourselves with this new technology and align our professional work with them to get most out of the new opportunities in this field.

Hold your breath Mega disruptions have begun!

Author Bio

Qualification: CA in Job / Business
Company: N/A
Location: NAVI MUMBAI, Maharashtra, IN
Member Since: 17 Sep 2017 | Total Posts: 3

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