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Abstract

ple accounts opened with similar KYC data</li></ul><h1 id="7ce3">Risk Assessment and Predictive Analytics</h1><p id="bcbc">Financial institutions are exposed to various risks, be it from competitors, creditors, debtors, regulatory authorities, or the various markets (capital, commodity, forex, etc.). Different ML techniques can be implemented to analyze the risk drivers and make predictions into the future and/or develop risk models.</p><p id="b684">For example:</p><ul><li>How likely is it that a particular borrower will default on its future obligations, given its historical payment behavior and other characteristics (age, income, family size, address, etc.)?</li><li>Estimate and predict Loss Given Default (LGD) at customer or portfolio levels</li><li>Analyze and predict market landscape</li><li>Investment banks can develop various risk models and scenarios to allow actionable, data-driven insights</li></ul><h1 id="7d4f">Customer Data Management and Analytics</h1><p id="1edb">Financial institutions are inundated with humongous data volumes — both structured and unstructured — with the latter being more challenging to manage, process, and gain insights from.</p><p id="2a56">ML and AI tools, such as Natural Language Processing, data mining, and text analytics, can transform this erstwhile cumbersome data management exercise into a unique opportunity to learn more about clients and identify actionable insights to drive new revenue opportunities.</p><p id="6e40">Clustering and segmentation algorithms can be utilized to segment similar customers together. Customer segmentation would allow targeted marketing campaigns and provide extra support to customers with a high churn risk.</p><p id="0fa2">Customer data can also be utilized to perform a <a href="https://en.wikipedia.org/wiki/Customer_lifetime_value">customer lifetime value analysis (CLV)</a>. Customers with high CLV should be identified and appropriately managed to derive the maximum benefit from the relationship in terms of both monetary benefit and growth.</p><h1 id="06e9">Personalized Services</h1><p id="a615">Somewhat related to the previous point, ML can also be utilized to provide personalized and tailored services to the clients — thereby ensuring a long-term and mutually beneficial relationship.</p><p id="86e1">Data analytics also enables the creation of personalized marketing that offers the right product to the right prospect at the right time and on the right device. Data mining is widely used for target selection to identify potential customers for a new product. The behavioral, demographic, and historical purchase data is used to build a model that predicts the probability of a customer/prospect’s response to a promotion or an offer.</p><p id="80ae">Recommendation engines (whether <a

Options

href="https://en.wikipedia.org/wiki/Collaborative_filtering">collaborative</a>, <a href="https://en.wikipedia.org/wiki/Recommender_system#Content-based_filtering">content-based</a>, or <a href="https://en.wikipedia.org/wiki/Recommender_system#Hybrid_recommender_systems">hybrid</a>) can analyze and filter a user’s activity to suggest to him the most relevant and accurate products or services.</p><h1 id="579c">Customer Support</h1><p id="22f7">AI-powered virtual assistants make it easier for financial institutions to allow for a channel-agnostic, consistent customer service experience that empowers customers to ask the virtual assistant for routine information, directions, or assistance on-demand. These virtual assistants (also referred to as chatbots) also save employees’ time.</p><h1 id="4861">Algorithmic Trading</h1><p id="5556">Although it is sometimes frowned upon, algorithmic trading is being carried out by large financial institutions and brokers at a massive scale. These algorithms place market orders in a pre-programmed fashion while automatically accounting for volume, price, and time variables; and leverage the computational processing speed of computers. The execution speed is thought of being unfair to regular traders since their transactions can lag the algorithmic trades and put them at a disadvantage.</p><h1 id="fbdf">Forecasting Through Time Series Analysis</h1><p id="1952">Time series forecasting is different from regular ML predictions since it adds an explicit order dependence between historical observations: a time dimension. Using historical trend, seasonality, and any noise in the data, time series forecasting attempts to predict a variable's values at a given future point in time, either with or without any other dependent variables.</p><p id="3d8f"><a href="https://en.wikipedia.org/wiki/Recurrent_neural_network">Recurrent Neural Networks (RNN)</a>, or more specifically, <a href="https://en.wikipedia.org/wiki/Long_short-term_memory">Long Short Term Memory (LSTM) networks</a> are usually utilized for time series forecasting. When it comes to the world of finance, finance professionals usually utilize time series forecasting to predict future sales volume, stock prices, demand for a product, etc.</p><h1 id="63d5">Conclusion</h1><p id="83ac">The use of data science by finance professionals goes beyond fraud, risk management, and customer analysis. Financial institutions can harness machine learning algorithms to automate business processes and improve security, as well.</p><p id="3728">Feel free to reach out to <a href="https://www.finlyticshub.com/">me</a> if you would like to discuss anything related to data analytics, machine learning, financial or credit analysis.</p><p id="ecf9">Till next time, code on!</p></article></body>

How to Harness the Power of Data Science in Finance

A brief introduction to certain use cases whereby financial institutions can effectively utilize data science and machine learning techniques

The world of Finance has always been about data. One could even argue that finance professionals utilized data in their daily operations even before the advent of data science, machine learning, and artificial intelligence. The most significant proof lies in the development of FICO scores way back in 1958.

The advancements in computational processing capabilities, ease of access to big data, and the development of complex algorithmic models have only served to fuel the rapid uptake of ML and AI by finance professionals. It is no wonder then that the US’ largest bank and the world’s 7th largest bank in terms of total assets, JPMorgan Chase & Co, invests $11bn per annum in new technologies.

Let us look at specific use cases where data science has helped finance professionals and financial institutions be more effective and efficient.

Fraud Detection and Prevention

Ensuring customer data security and minimizing fraudulent transactions are critical to a financial institution’s ongoing operations and are usually enforced and mandated by the regulatory authorities. ML algorithms can learn from historical data and identify unusual behavior, patterns, or transactions. Such algorithms include:

Anomaly detection algorithms to identify and flag suspicious transactions and potentially put a hold on the transaction till the customer confirms it to be genuine
Clustering algorithms can segregate and collect together abnormal transactions to be investigated further

ML can assist financial institutions in recognizing:

Fake insurance claims, based on historical patterns found in genuine claims
Suspiciously high-value or high-volume transactions
Identity theft
Money laundering
Multiple accounts opened with similar KYC data

Risk Assessment and Predictive Analytics

Financial institutions are exposed to various risks, be it from competitors, creditors, debtors, regulatory authorities, or the various markets (capital, commodity, forex, etc.). Different ML techniques can be implemented to analyze the risk drivers and make predictions into the future and/or develop risk models.

For example:

How likely is it that a particular borrower will default on its future obligations, given its historical payment behavior and other characteristics (age, income, family size, address, etc.)?
Estimate and predict Loss Given Default (LGD) at customer or portfolio levels
Analyze and predict market landscape
Investment banks can develop various risk models and scenarios to allow actionable, data-driven insights

Customer Data Management and Analytics

Financial institutions are inundated with humongous data volumes — both structured and unstructured — with the latter being more challenging to manage, process, and gain insights from.

ML and AI tools, such as Natural Language Processing, data mining, and text analytics, can transform this erstwhile cumbersome data management exercise into a unique opportunity to learn more about clients and identify actionable insights to drive new revenue opportunities.

Clustering and segmentation algorithms can be utilized to segment similar customers together. Customer segmentation would allow targeted marketing campaigns and provide extra support to customers with a high churn risk.

Customer data can also be utilized to perform a customer lifetime value analysis (CLV). Customers with high CLV should be identified and appropriately managed to derive the maximum benefit from the relationship in terms of both monetary benefit and growth.

Personalized Services

Somewhat related to the previous point, ML can also be utilized to provide personalized and tailored services to the clients — thereby ensuring a long-term and mutually beneficial relationship.

Data analytics also enables the creation of personalized marketing that offers the right product to the right prospect at the right time and on the right device. Data mining is widely used for target selection to identify potential customers for a new product. The behavioral, demographic, and historical purchase data is used to build a model that predicts the probability of a customer/prospect’s response to a promotion or an offer.

Recommendation engines (whether collaborative, content-based, or hybrid) can analyze and filter a user’s activity to suggest to him the most relevant and accurate products or services.

Customer Support

AI-powered virtual assistants make it easier for financial institutions to allow for a channel-agnostic, consistent customer service experience that empowers customers to ask the virtual assistant for routine information, directions, or assistance on-demand. These virtual assistants (also referred to as chatbots) also save employees’ time.

Algorithmic Trading

Although it is sometimes frowned upon, algorithmic trading is being carried out by large financial institutions and brokers at a massive scale. These algorithms place market orders in a pre-programmed fashion while automatically accounting for volume, price, and time variables; and leverage the computational processing speed of computers. The execution speed is thought of being unfair to regular traders since their transactions can lag the algorithmic trades and put them at a disadvantage.

Forecasting Through Time Series Analysis

Time series forecasting is different from regular ML predictions since it adds an explicit order dependence between historical observations: a time dimension. Using historical trend, seasonality, and any noise in the data, time series forecasting attempts to predict a variable's values at a given future point in time, either with or without any other dependent variables.

Recurrent Neural Networks (RNN), or more specifically, Long Short Term Memory (LSTM) networks are usually utilized for time series forecasting. When it comes to the world of finance, finance professionals usually utilize time series forecasting to predict future sales volume, stock prices, demand for a product, etc.

Conclusion

The use of data science by finance professionals goes beyond fraud, risk management, and customer analysis. Financial institutions can harness machine learning algorithms to automate business processes and improve security, as well.

Feel free to reach out to me if you would like to discuss anything related to data analytics, machine learning, financial or credit analysis.

Till next time, code on!