5 Ways Data Science Changed Finance

Summary

This 365 Data Science special explores the top 5 ways data science is revolutionizing finance. It covers fraud prevention, anomaly detection, customer analytics, risk management, and highlights algorithmic trading as the most impactful. The script delves into how machine learning and deep learning enhance security, predict consumer behavior, and automate trading to minimize human error, emphasizing data as the new competitive edge in the financial industry.

Takeaways

• 🔒 Data Science has revolutionized Fraud Prevention in finance by using algorithms to detect unusual activities and protect against identity theft and credit card fraud.
• 🕵️‍♂️ Anomaly Detection is employed to identify post-facto unusual trading patterns, particularly to catch illegal insider trading, using advanced deep learning models.
• 📊 Customer Analytics allows financial institutions to segment consumers based on behavioral trends and socio-economic characteristics, helping to predict and manage client value and financial strategies.
• 💰 Risk Management in finance has been enhanced by data science through the development of real-time scoring models that assess and mitigate uncertainties and vulnerabilities in investments.
• 🤖 Algorithmic Trading represents a significant leap, with machines making high-speed trades based on algorithms, reducing human error and exploiting market opportunities efficiently.
• 📉 The 2008 financial crisis highlighted the need for better risk models, leading to the adoption of data science to create more reliable credit scoring systems and prevent similar downturns.
• 🚫 The script mentions a downside of algorithmic trading where imprecise algorithms can lead to significant market losses, as seen in the Dow Jones plummet in February 2018.
• 🛑 Human intervention may still be necessary in unprecedented events, such as the drone strike on Saudi Arabia's oil refinery in 2019, to prevent algorithmic trading models from causing market chaos.
• 📈 The scarcity of arbitrage opportunities and the even playing field due to algorithmic trading have led financial institutions to seek a competitive edge through exclusive data access.
• 📊 Data has become a highly valued commodity in the finance industry, with institutions investing heavily to gain insights and construct superior models for market advantage.
• 🌐 The script emphasizes the importance of Machine Learning and Deep Learning as integral parts of Data Science, enabling the construction of self-evolving algorithms for future predictions.

Q & A

How has Data Science transformed the financial industry?

Data Science has transformed the financial industry by introducing automated algorithms and complex analytical tools that work hand-in-hand to provide insights and predictions, leading to advancements in areas such as fraud prevention, anomaly detection, customer analytics, risk management, and algorithmic trading.

What is the role of Machine Learning (ML) and Deep Learning (DL) in data science?

Machine Learning and Deep Learning are aspects of data science that use modeling algorithms to find links between data, extract insights, and make future predictions. They allow for the construction of self-evolving algorithms given enough time and information.

How does data science aid in fraud prevention within financial institutions?

Data science aids in fraud prevention by using methods like random forests to determine if there are sufficient factors to indicate suspicion. It also supports security advancements with biometric recognition and additional authentication layers to lower the chances of identity theft.

What is anomaly detection in finance, and how does deep learning contribute to it?

Anomaly detection in finance is the process of identifying unusual or suspicious activities, such as illegal insider trading, after they occur. Deep learning, through Recurrent Neural Networks and Long Short-Term Memory models, helps create algorithms that can spot trading histories that are well above the norm.

How does customer analytics in finance benefit from data science?

Customer analytics benefits from data science by using unsupervised ML techniques to segment consumers into clusters based on socio-economic characteristics. Predictive models are then constructed to determine the expected worth of each client, allowing financial institutions to allocate resources efficiently.

What is the significance of risk management in the financial industry, and how does data science support it?

Risk management is crucial for ensuring stability and minimizing uncertainty in financial deals. Data science supports this by developing risk analytics, which involves identifying, ranking, and monitoring uncertain interactions to prioritize and address vulnerabilities in investments.

What is algorithmic trading, and how does it impact the financial markets?

Algorithmic trading is the process where a machine makes trades on the market based on a predefined algorithm. It can execute trades multiple times per second across various markets without human intervention, reducing opportunity costs and human errors. It has led to high-frequency trading and increased market efficiency.

How have trading algorithms evolved to prevent market instability?

Trading algorithms have become more complex to prevent market instability, incorporating reinforced learning models where mistakes are penalized, and hyperparameters are adjusted based on performance to make better estimations.

What was the impact of the 2018 Dow Jones plummet on trading algorithms?

The 2018 Dow Jones plummet, caused by trading algorithms misinterpreting a false signal, led to a quick market crash. This incident prompted the development of more sophisticated algorithms to prevent similar market freefalls.

How has data science influenced the value of data in the financial industry?

Data science has made data the most valuable commodity in the financial industry. Financial institutions are investing heavily in exclusive data rights to construct better models and gain a competitive edge.

What is the role of data in the competition among financial institutions today?

In today's financial industry, data plays a crucial role in gaining a competitive edge. With more information, institutions can develop superior models, leading to an increased focus on data acquisition and utilization over traditional analyst or quant roles.

Outlines

00:00

🚀 Data Science in Finance: Introduction and Fraud Prevention

The video script introduces a special on how data science is revolutionizing the finance industry, highlighting the impact of machine learning (ML) and deep learning (DL) in creating self-evolving algorithms for predictive insights. It emphasizes the importance of understanding ML and DL as integral parts of data science. The script then delves into the top 5 applications, starting with fraud prevention, explaining how financial institutions use data science to detect and respond to fraudulent activities such as identity theft and credit card fraud. It mentions the use of random forests and pattern recognition methods to flag suspicious transactions, alongside advancements in security measures like 3D passwords and text message confirmations.

05:03

🔍 Anomaly Detection and Customer Analytics in Finance

The script continues with the topic of anomaly detection in finance, which focuses on identifying irregular activities post-occurrence, such as illegal insider trading. It describes how deep learning models, including Recurrent Neural Networks and Long Short-Term Memory models, are used to analyze trading patterns and detect anomalies. Moving on to customer analytics, the script explains how financial institutions use past behavioral trends and socio-economic characteristics to predict consumer actions, segment consumers into clusters, and estimate future gains from each client. This allows for efficient resource allocation and strategic decision-making in catering to profitable consumers and minimizing losses from unprofitable ones.

10:07

📊 Risk Management and Algorithmic Trading in Finance

The script discusses the importance of risk management in finance, detailing how data science aids in identifying, ranking, and monitoring uncertainties to minimize human error. It explains the roles of risk management analysts and quantitative analysts, and how data scientists have taken on these roles with the necessary skills. The use of customer transaction data to create real-time scoring models is highlighted, showing how banks have moved away from risky lending practices post-2008 financial crisis. The video concludes with the most significant contribution of data science to finance: algorithmic trading. It describes how algorithms make high-speed, volume-based trades based on a set of rules, with reinforcement learning models adjusting parameters based on performance. The script also touches on the efficiency and precision of algorithmic trading, as well as the risks and the need for human intervention in unpredictable market events.

Keywords

💡Data Science

Data Science is an interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data. In the context of the video, it is central to how financial institutions are leveraging data to drive innovation and efficiency. The script discusses its role in fraud prevention, anomaly detection, customer analytics, risk management, and algorithmic trading, showcasing its wide-ranging impact on the financial industry.

💡Machine Learning (ML)

Machine Learning is a subset of artificial intelligence that enables systems to learn from and make decisions based on data. The video script emphasizes ML's importance in creating self-evolving algorithms that can predict future trends and detect anomalies in financial data, such as identifying fraudulent activities or unusual trading patterns.

💡Deep Learning (DL)

Deep Learning is a specialized form of machine learning that uses neural networks with many layers (deep neural networks) to model and understand complex patterns in data. The script refers to DL in the context of anomaly detection, where it is used to analyze trading patterns to identify potential illegal insider trading.

💡Fraud Prevention

Fraud Prevention is the act of detecting and stopping fraudulent activities such as identity theft and credit card scams. The video explains how data science, particularly through the use of ML algorithms like random forests, helps financial institutions to automatically detect suspicious activities and protect clients from financial losses.

💡Anomaly Detection

Anomaly Detection involves identifying unusual patterns or outliers in data that do not conform to expected norms. In the video, it is used to catch illegal insider trading by analyzing trading patterns before and after the release of non-public information, using models like Recurrent Neural Networks and Long Short-Term Memory networks.

💡Customer Analytics

Customer Analytics is the process of analyzing customer data to understand behavior and predict future actions. The script describes how financial institutions use socio-economic characteristics to segment consumers and estimate their future value, allowing for more efficient resource allocation and targeted marketing strategies.

💡Risk Management

Risk Management is the process of identifying, assessing, and prioritizing potential risks to minimize their impact on an organization. The video highlights the role of data science in developing risk analytics, where adaptive real-time scoring models are used to assess the creditworthiness of clients and manage uncertainties in the market.

💡Algorithmic Trading

Algorithmic Trading refers to the use of computer programs to execute trades automatically based on predefined algorithms. The script explains how this method has revolutionized the trading process by reducing human error, exploiting arbitrage opportunities, and increasing market efficiency, although it also mentions the potential for significant losses if the algorithms are imprecise.

💡Arbitrage

Arbitrage is the practice of taking advantage of a price difference between two or more markets to make a profit. The video discusses how algorithmic trading models look for and exploit these inconsistencies to ensure certain profits, emphasizing the speed and efficiency of these trades.

💡High-Frequency Trading (HFT)

High-Frequency Trading is a type of algorithmic trading that involves a large number of trades executed at extremely fast speeds, often within seconds. The script mentions HFT as a key advantage of algorithmic trading, allowing for immediate response to market opportunities and reducing the time lag between signal detection and trade execution.

💡Data as a Commodity

In the context of the video, Data as a Commodity refers to the increasing value and strategic importance of data in gaining a competitive edge in the financial industry. Financial institutions are investing heavily in acquiring exclusive data to build better models and make more informed decisions, highlighting a shift from traditional human expertise to data-driven insights.

Highlights

Data Science and Machine Learning have ushered in a new era in finance, enhancing traditional methods with automated algorithms and complex analytical tools.

Machine Learning (M-L) and Deep Learning (D-L) are integral to Data Science, enabling the construction of self-evolving algorithms through modeling and prediction.

Fraud Prevention in finance leverages data science techniques such as random forests to identify suspicious activities and protect against financial losses.

Anomaly Detection in finance aims to identify post-facto unusual events, like illegal insider trading, using advanced deep learning models.

Customer Analytics allows financial institutions to predict consumer behavior based on past trends and socio-economic characteristics, optimizing resource allocation.

Unsupervised Machine Learning techniques are used to segment consumers into clusters and assign lifetime evaluations, aiding in strategic decision-making.

Risk Management in finance is enhanced by data science, which helps in modeling and managing uncertainties to ensure stability and predictability.

Adaptive real-time scoring models, developed using customer transaction data, help in assessing the creditworthiness of potential clients.

Algorithmic Trading, where machines execute trades based on algorithms, has significantly reduced human error and opportunity costs in the market.

Reinforced learning models in trading algorithms adjust hyperparameters based on performance, optimizing decision-making rules over time.

High-frequency trading enabled by algorithmic models can capitalize on arbitrage opportunities instantly, increasing market efficiency.

The evolution of trading algorithms has led to a scarcity of arbitrage opportunities as they are exploited as soon as they arise.

Data has become the most valuable commodity in finance, with institutions investing heavily to gain exclusive access to information.

The financial industry is now in an era where data, rather than human analysts, is the key to gaining a competitive edge.

Data Science has revolutionized finance by improving security, reducing human error, and automating trading models.

The '365 Data Science Program' is designed to train individuals in various aspects of data science, regardless of their background.

Human intervention may still be required in trading algorithms to address unprecedented events that models cannot predict.

Transcripts

Welcome to this 365 Data Science special where we’ll explore the top 5 ways data science

is reinventing Finance!

Ever since its genesis, Data Science has helped transform many industries.

For decades financial analysts have relied on data to extract valuable insights, but

the rise of Data Science and Machine Learning has brought upon a new era in the field.

Now, more than ever, automated algorithms and complex analytical tools are being used

hand-in-hand to get ahead of the curve.

But before we proceed, we need to very briefly explain some of the terminology we’ll be

using.

Machine Learning, or M-L, and Deep Learning, or D-L, are different aspects of data science

that use modelling algorithms to find links between data, extract insights and draw predictions

for the future.

They are an important part of Data Science and allow us to construct algorithms that

evolve on their own, given enough time and information.

Okay, now that this is out of the way, let’s explore the top 5 ways in which financial

institutions use these methods to their advantage, shall we?

Number 5: Fraud Prevention Fraud prevention is a part of financial security

that deals with fraudulent activities, such as identity theft and credit card schemes.

Abnormally high transactions from conservative spenders, or out of region purchases often

signal credit card fraud.

Whenever such are detected, the cards are usually automatically blocked, and a notification

is sent out to the owner.

That way, banks can protect their clients, as well as themselves and even insurance companies,

from huge financial losses in a short period of time.

The opportunity costs far outweigh the small inconvenience of having to make a phone call

or issue another card.

The role data science plays here comes in the form of random forests and other methods

that determine whether there are sufficient factors to indicate suspicion.

Surely, security advancements with facial or fingerprint recognition have added layers

of authentication which have lowered the chances of identity theft, as well.

3D passwords, text messages confirmation and PINT codes have also massively backed the

safety of online transactions.

However, we’re more interested in the initial security measurements we mentioned.

Those pattern recognitions also require the use of ML algorithms, so data science has

substantially improved fraud prevention in more ways than one.

Number 4: Anomaly Detection Unlike Fraud Prevention, the goal here is

to detect the problem, rather than prevent it.

The reason is that we can’t classify an event “anomalous” as it happens but can

only do so in the aftermath.

The main application of this anomaly detection in finance comes in the form of catching illegal

insider trading.

In today’s financial world it isn’t always easy to spot trading patterns with a naked

eye.

Of course, any trader can strike gold and accurately predict the boom or collapse of

a given equity stock occasionally, but there exist ways of determining what is out of the

norm.

Enter, deep learning.

Through a mix of Recurrent Neural Networks and Long Short-Term Memory models, data scientists

can create anomaly-detection algorithms.

Such an algorithm can spot whenever somebody’s trading history is well-above the norm, both

for them as an entity, and the market as a whole.

The way it works is, they analyse the trading patterns before and after the internal announcement

of non-public information like the release of a new product or an upcoming merger.

Then, based on the volume and frequency of the transactions, the model can decide if

somebody is using non-public information to exploit the market and take advantage of innocent

investors.

Thus, data science has had a huge impact on catching and punishing illegal trading in

the industry.

Moving on to Number 3: Customer Analytics.

Based on past behavioral trends, financial institutions can make predictions on how each

consumer is likely to act.

With the help of socio-economic characteristics, they’re able to split consumers into clusters

and make estimations on how much money they expect to gain from each client in the future.

Knowing this, they can decide which ones to cater to and how to appeal to them more.

Similarly, they can cut their losses short on consumers who will make them little or

no money.

In short, it allows them to distribute their savings in the most efficient way.

For example, insurance companies often use this technique to assign lifetime evaluations

to each consumer.

And while this is not the most precise technique, it does prove to be very solid in practice.

So how does Data Science fit into this?

Using unsupervised M-L techniques, the company splits consumers into distinct groups based

on certain characteristics, such as age, income, address, etc.

Then, by constructing predictive models, they determine which of these features are most

relevant for each group.

Depending on this information, they assign expected worth of each client.

Having quantified the value or the range of values of each consumer, they can decide who

is worth keeping and who isn’t, which helps them allocate their savings best.

If you want to learn more about customer analytics and many other data science topics, we’ve

got you covered.

We’ve created ‘The 365 Data Science Program’ to help people enter the field of data science,

regardless of their background or future interests.

We have trained more than 350,000 people around the world and we’re committed to continuing

to do so.

If you are interested to learn more about the program, you can find a link in the description

that will also give you 20% off all plans.

Now, back to our countdown with…

Number 2: Risk Management Another important factor in finance is stability,

a.k.a. risk management.

Investors and higher-ups don’t like uncertainty when it comes to major deals, so there exists

a need to measure, analyse and predict risk.

Of course, the short term for that is “risk analytics”, and data science has provided

great help in developing that part of the financial industry.

So, let’s explore it in more detail.

Risk can be many things – it can be uncertainty about the market, it can be an influx of competition,

or it can be some customer trustworthy-ness.

Depending on what type it is, we use different ways to model and manage it.

Overall, risk management is a complex field requiring knowledge across finance, math,

statistics and more.

You may have heard of positions called ‘risk management analysts’ or ‘quantitative

analysts’.

However, a current-day data scientist has the necessary skills for both previous positions.

Therefore, financial institutions utilize data science to minimize the probability of

human error in the process.

But how is that achieved in practice?

The main approach dictates that the first step is identifying and ranking all the uncertain

interactions.

Then, we monitor them going forward, and prioritize and address the ones that make our investments

most vulnerable at a given time.

Banks tend to use customer transactions data and other available information to create

adaptive real-time scoring models.

Those frequently update how “risky” each consumer is and whether they are suitable

for a credit loan or mortgage.

In fact, since the Great Recession of 2008, banks have shied away from giving out the

infamous NINJA loans.

For anybody unfamiliar with the term, NINJA stands for: No Income, No Job or Assets.

Instead, they’ve opted to use data science and create more reliable risk score models

to determine the creditworthiness of potential clients.

This just goes to show you how through machine learning, the banking industry has evolved

and effectively put a soft brake to prevent a potential repeat of the crisis.

That being said, neither of the topics we discussed so far are the main contribution

data science has had on the financial industry.

That accolade belongs to number one on our list: Algorithmic Trading.

To explain it briefly, a machine makes trades on the market based on an algorithm.

These trades can happen multiple times every second with various degrees of volume and

do not need to be approved by a stand-by analyst.

These trades can be in whatever market we want, or even multiple markets simultaneously.

Thus, algorithmic trading has mitigated many of the opportunity costs that come from missing

a trading opportunity by hesitation, as well as other human errors.

In their foundation, these algorithms consist of a set of rules, which steer the decisions

to trade or not.

On top of that, we usually see a reinforced learning model, where mistakes are heavily

penalized.

Based on how well the model performs, it adjusts the hyper parameters to make better estimations

going forward.

In layman’s terms, the model adjusts the values for each rule, based on performance.

Most notably, we see algorithms that find and exploit arbitrage opportunities.

In other words, they find inconsistencies and make trades which lead to certain profits.

The huge upside of algorithmic trading is that it can be high frequency.

In other words, the moment the algorithm finds an opportunity to make a profit, it will.

However, these algorithms don’t always have to trade all the time.

The way they work is the following: they develop conditions that make up a “signal”.

Once they are met, this signal is sent out to the algorithm, and it makes a trade.

The requirements for these conditions are so well-established that it takes fractions

of a second between the signal and the trade to occur, so the process is essentially instantaneous.

However, sometimes these conditions aren’t met for months on end.

Sometimes, all the movements of the equity stock or security are simply noise, so the

algorithm doesn’t twitch.

This makes algorithmic trading so successful because it’s not trigger-happy and can wait

out to make sure the moment is correct.

A downside these algorithms used to have, was that if they were imprecise, it could

lead to huge losses due to the lack of human supervision.

For instance, in February 2018, the price of Dow Jones plummeted after several trading

algorithms interpreted a false signal.

A devastatingly quick snowball effect emerged as other algorithms followed suit and the

stock price fell by $80 in mere minutes.

After that, many algo-trading models were made much more complex in order to prevent

the market from going into freefall.

Sometimes though, something unprecedented happens, and human intervention is needed

to suspend the models.

For example, in September 2019 a drone strike in Saudi Arabia set ablaze the world’s largest

oil refinery.

This caused huge uncertainty in the market and a high volatility of the prices of crude

oil all around the world.

Since these events cannot be predicted, regardless of how well-trained the model is, many investors

tend to pause their trading algorithms.

Even though huge gains can be made, so too can huge losses.

As we already mentioned, CEOs are risk-averse and prefer stability.

Since the vast and fast development of such trading algorithms, the playing field is very

much evened out when competitors have the same access to information.

This makes arbitrage opportunities very scarce, since they are often exploited immediately.

In turn, this has led to great efficiency in the market, so hedge funds and investment

banks need to look for an edge over the competition elsewhere.

Here lies the latest change data science has brought onto the finance industry.

Nowadays, data has become the hottest commodity that results in getting an edge over the competition.

Financial institutions are spending huge amounts of money to get exclusive rights to data.

By having more information, they can construct better models and get ahead.

Thus, the most valuable commodities are no longer the analysts themselves or the quants

that help design these algorithms, but the data itself.

So, this is how the introduction of data science has truly revolutionized the financial industry.

From leaps in security and loss prevention to automated trading models that decrease

human error, we’ve certainly entered a new era for the industry.

More than ever before, data is the resource everybody is fighting over.

If you liked this video, don’t forget to hit the “like” or “share” button!

And if you’d like to become an expert in all things data science, subscribe to our

channel for more videos like this one.

Thanks for watching!