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Data analytics in finance broucher
- 1. Data Analytics in Finance www.positivenaick.com Finding the secret sauce to predict customer trends in finance.
- 2. Overview of Analytics Financial institutions exist not just by providing a safe place for customers to save their money, but also provide avenues where customers can invest their money and procure loans for their own expenses. A healthy finance industry is a good sign of a strong national economy. When money flows freely from and to consumers, it enriches all other industries. That’s why it’s important to remember that financial institutions need to operate at peak efficiency, ensuring great returns for themselves as well as their customers, to ensure that every other cog in the economy’s machine runs smoothly. Data analytics works by tapping into the rivers of data financial institutions are already collecting and storing, and adding new methods for data collection. Once all this data is collected, we can use computers to analyze and find patterns in data. These patterns give us insights into customer habits, market trends, operational inefficiencies, and bottlenecks. Using these insights, we can find new methods and strategies to improve revenue, retain customers, and increase profits. 1 Credit Risk Fraud, AML Liquidity Risk Default Management Capital Allocation Analysis Credit Loss Provision Net Interest Margin Variance Structured Finance Analysis Liquidity Analysis Customer Profitability Cross Sell Analysis Customer Acquisition Customer Behavior Analysis Customer Loyalty Campaign Analysis Call Center Analytics Channel Profitability Location Profitability Product Profitability Customer Life Time Value Profitability RiskRelationship Marketing Financial Analytics Funds Marturity Analysis Collections and Credit Exposure Asset & Liability Management
- 3. 2 Financial Risk Modeling The need to weigh financial risks is important, since it can show which customers can be given loans without worry of repayment. Additionally, it can also look into investment opportunities, to gauge whether a particular investment is likely to yield better returns compared to another. Business Need Here, the expected outcome is to categorize customers and investments into two primary buckets, whether they’re high-risk or low-risk. Based on this, the financial institution can choose at their discretion about whether to lend or invest. OutcomeApproach To gauge financial risk, we can look at customer financial records, their banking history, credit history, and repayment histories to find out whether a customer has enough repayment ability to safely provide a loan. Additionally, a similar approach can be used to gauge risks in investment opportunities based on their past performance, as well as their current statuses. In the past, risk modeling used to be done manually, on a qualitative basis. Although this method does have some merits, it has the potential to miss out on some key details in some cases, falsely identifying risky investments as safe. It can even mis-identify safe investments as risky. Financial Data Economic Data The BCM Market Risk Model Outlook Favorable or Unfavorable Monetary Data Financial risk model
- 4. 3 The expected outcome here is to have reports that provide solid information on why a specific investment or lending failed, thereby helping give more information to the financial institution about which investments should be prioritized and which should not be accepted. Outcome Diagnostic Analytics By finding out at-risk investments and customers, financial institutions can decide with concrete knowledge that some investments and lending are not worth the risk, reducing the losses incurred by the institution. Business Need Diagnostic Analytics looks into data to understand the basics of what happened. This can be for a mutual fund that didn’t yield any promising returns, or a set of customers who couldn’t pay back their loans. Approach By looking into past repayments for customers, or financial records for companies seeking investment, diagnostic analytics will take past and current financial performance into account and produce a report on why a certain investment or loan failed. This can be used for future lending activities. Causal / driver analysis Explanatory statistical models Enthusiastic consumption of findigs / insights Detailed dashboards Implementing recommenda -tions can be difficult Diagnostic Analytics
- 5. 4 Customer Analytics By analyzing customers, it is possible to discern high-value customers from high-risk customers. High value customers can be offered more products to increase their customer relationship with the financial institution, while high-risk customers can be denied or charged higher interest rates on loans. Business Need By creating different buckets for customers, financial institutions can offer more and better products to high-value customers to retain their patronage, as well as loyalty rewards. High-risk customers can be charged a higher interest rate to accommodate their higher risk, or be denied loans entirely. OutcomeApproach The algorithm will look at past repayments, credit scores, and outstanding dues, as well as their current economic situation to discern which category a customer fits into. Not all customers are equal. Some customers have higher spending potential, making them better safer opportunities for loans. But there are other customers who won’t be as reliable when it comes time to collect for loan repayments. New visitorNew visitor Repeat Visitor Existing Customer Existing customer Clicked adwords campaign interested in auto insurance Has Auto Policy Has Auto Policy Spend 5+ minutes on side this week Has Home Policy Upcoming Renewal In Atlanta, GA Last Visited three months ago upsell opportunity
- 6. 5 By optimizing asset lending and loan servicing, financial institutions can expect to find greater revenue from their investment and loan portfolios. Outcome Transaction Data Analytics With transaction data analytics, financial institutions can maximize the potential number of buyers and increase purchase price of assets the financial institution puts out. Business Need Transaction analytics uses data, technology and advanced quantitative analysis to drive accurate observations and insights. This is basically the usage of knowledge and raw data to replace speculation. Approach By analyzing the data between past transactions for assets that are delivered to buyers for investment and expenditure options, we can gain insights into what buyers will buy, and what kind of loans are attractive to borrowers.
- 7. 6 The Process of Analytics Adoption Adopting analytics into finance is not a one-size-fits-all solution. What works for one bank may not work for another. The magnitude, size, goals, and complexity of the implementation all factor into the equation. There are some questions companies must answer before adopting analytics. • Identifying internal use cases • Measuring Analytics • Finding Required Talent • Technical requirements Finding Required Talent Technical Requirements Identifying Internal Use Cases measuring Analytics Data analytics needs people with specialized skills to implement and monitor properly. Different retailers will have differing requirements in finding this talent. There are some key questions to be asked before finding the resources. Can they afford to have an in-house team working on analytics? Do they need a permanent analytics team in-house all the time? Can they use an analytics firm to fulfil their needs? By asking these questions, a company can understand what kind of analytics requirement they can employ. Before implementing analytics, it’s important to identify a set of problems the solution will eventually be tailored around. These problems are what will create the base skeleton upon which the insights are discovered, and solutions are crafted. These use cases should also be classified according to severity or magnitude of impact. There can be a plethora of use cases in any retailer’s working space, but some issues will create greater impact than others. When working on a budget, it’s important to ensure that companies focus on their biggest priorities. After these use cases are set, the next step is to measure the data and the impact of the future solutions. The next step is to measure the impact of those changes. This means asking a few key questions. What are the performance goals after deploying the solution? How are these goals going to be measured? Does our organization have the tools necessary to measure them? This will provide valuable insight into finding out whether or not the deployed solution is actually performing as intended, or if it is causing any unintended side effects. The final thing to consider when looking into analytics is learning what are the technical requirements for the analytics results you’re looking for. Some financial institutions are focused on preventing loan defaulting, while others are looking to broaden their investment portfolios. Different goals have different technical requirements, and it’s imperative that companies looking to employ analytics solutions have a clear idea of what they need, what they already have on hand, and what they need to acquire.
- 8. We hope this gave you better insight into how Data Analytics can help your company reach new business goals. If you have any questions, please contact us using the details below. Thank You PositiveNaick Analytics Ltd. No177,1st floor, LM Tech Park, 1st Main Rd, Nehru Nagar, Kottivakkam, Chennai, Tamil Nadu 600041. Email: [email protected] Website: www.positivenaick.com Phone: +91-44 4857 6162