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  • 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 eventanomalousas 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 isrisk 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 thingsit 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 howriskyeach 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 thelikeorsharebutton!

  • 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!

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

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データサイエンスが金融を変えた5つの方法 (5 Ways Data Science Changed Finance)

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    林宜悉 に公開 2021 年 01 月 14 日
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