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About Actionable Insights From Selected Metrics And KPIs Cited In Point

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    ACTIONABLE INSIGHTS FROM SELECTED METRICS AND KEY PERFORMANCE INDICATORS (OR KPIS) CITED IN POINT

    Actionable insight is the result of data-driven analytics in the business world. Vast amounts of data are analyzed to find patterns such as spending habits, market upheavals and downturns, and other market and financial patterns that may help an analyst formulate a business plan.

    Data analytics is used to answer “why” questions by capturing large amounts of data and then running them through analytics software and data visualization tools. These tools in turn provide insights into the patterns that are inherent to the market. Once there is enough insight that a proper course of action can be made from it, then this result is considered as an actionable insight.

    This is often used in big data and marketing. Rich analytics tools with massive visualization options and smart analytics capabilities are becoming increasingly available through the cloud, giving even small companies the edge to compete in a tight business environment. With these tools, it is easier to derive actionable insights that can help management make correct decisions.

    1. Fraud Analytics

    False Positives and Fraud Detection

    a. If the result is True Positive and True Negative , then it’s an actionable insight. For True Positive , it’s a fraud detected and stop further transaction. For True Negative, it’s NOT a fraud and proceed with the transaction.

    b. If the result is False Positive and False Negative, then it’s not an actionable insight. Further action to be taken to investigate what went wrong.

    Fraud to Sales Ratio

    a. Fraud amount has to be lower than Sales amount, to show a functioning data analytic software

    Strong fraud indicators

    Unknown fraud types need strong indicators in order to convict a suspicious account is indeed fraudulent. In addition to providing a detection score, a fraud analytics dashboard needs to include top reasons why a score is determined in human-understandable reason codes. For unknown fraud, anomalous attributes showing how an account or transaction deviates from the global population provide additional indicators. https://www.datavisor.com/blog/top-five-insights-needed-for-unknown-fraud-analytics/

    2. Credit risk analytics

    Exposure at Default (EAD), Probability of Default (PD) and Loss Given Default (LGD) are metrics used by financial institutions to calculate their total loss when a borrower defaults a loan.

    A bank may calculate its expected loss by multiplying the variable, EAD, with the PD and the LGD:

    EAD x PD x LGD = Expected Loss

    If the bank assess the amount is too huge , ie high risk of net loss, it can decide to

    1. not to proceed with the loan,

    2. increase interest rate,

    3. reduce the total loan amount,

    4. extend longer repayment period

    BENEFITS AND PITFALLS OF ANALYTICS

    Innovation and Technology have important effects on Businesses. Irrespective of industry or organization, analytics serves advantages. As it will enable company to gain profit and create the outcomes their client’s request. Innovation and Technology foundations influence the way of life, effectiveness, and connections of a business (loginworks, n.d.).

    Business is critical to a nation’s economy because businesses provide opportunities, merchandise, and enterprises. Organizations are likewise the methods by which numerous individuals land their jobs. Organizations need resources for work since they require individuals to deliver and pitch their products and ventures to purchasers.

    With this, every business needs an appropriate approach for better performances and growth. This is where Analytics comes into the picture. Organizations gather information from various resources and industry. Among numerous advantages, using data can help companies spare a huge number of pounds, enhance their proficiency, build up their advertising techniques, support business growth and, fundamentally, separate themselves from contenders.

    The data analytics involve various operations on the data sets or tables available in databases. The operations include data extraction, data profiling, data cleansing and data deduping etc. https://www.rfwireless-world.com/Terminology/Advantages-and-Disadvantages-of-Data-Analytics.html

    BENEFITS OF ANALYTICS

    With technologies are on the rise, analysing big data helps to optimize efficiency in many different industries and improving performance enables business to succeed in a competitive world. One of the earliest adopters is the financial sector.

    Data analytics has a huge role in the banking and finance industries. Data analytics of Credit scores is one example that affects everyone. Scores are used to determine lending risk, detect and prevent fraud to improve efficiency and reduce risk for financial institutions (masters, n.d.).

    There are some benefits of analytics listed below:

    1. Better decision making

    With the rate and speed of information gathering, its empowering companies to make every decision made before better, swift and more productive activities.

    Analytics perhaps the best advantage of the Data analytics. With the first rate of right data and speed of information gathering empower organizations to make quicker and more educated activities. That is quite a crucial key in exceedingly challenging in businesses.

    2. Improved Performance

    Analytics has changed the traditional approaches to performance and deliver. Companies is using analytics to enhance the firm performances to meet their client’s request and achieve customer requirements to meet their commitments.

    3. Quality and consistency

    An analytics applied to set of information and data collected to discover unexpected insights hidden in the company’s vast historical and real-time databases (predisys, n.d.) to immediately identify problem areas or new opportunities for improvement.

    With ETL (extracted, transformed and loaded) Data collected quality output within the company from all various department and the reason for solid business choices data analytics can help business to gain quality and consistency.

    4. Increased cost effectiveness

    Data is easily store and comfortably managed using analytics. It helps to improve efficiency as well as help different groups access the appropriate data effortlessly (centric, n.d.). Companies convert their growing volumes of data into solid predictions and actionable insights with the LEAST amount of resource expenditure.

    PITFALL OF ANALYTICS

    Data science has gone through a rapid evolution, fueled by powerful open source software and more affordable and faster data storage solutions. Universities have adapted to the increasing demand as well and are graduating analytically trained students at an unprecedented pace. This evolution opens new and innovative pathways for many companies and individuals to make a difference to the bottom line. With this fast-paced evolution, however, a number of classic pitfalls are on the rise as well. By understanding those pitfalls and ways to avoid them, you can take advantage of innovations in data science and help your business perform to its maximum—data-proven—potential.

    1. Breach privacy of the customers as their information such as purchases, online transactions, subscriptions are visible to their parent companies. The companies may exchange these useful customer databases for their mutual benefits.

    2. the cost of data analytics tools vary based on applications and features supported. Moreover some of the data analytics tools are complex to use and require training. This increases cost to the company willing to adopt data analytics tools or softwares.

    3. The information obtained using data analytics can also be misused against group of people of certain country or community or caste.

    4. It is very difficult to select the right data analytics tools. This is due to the fact that it requires knowledge of the tools and their accuracy in analysing the relevant data as per applications. This increases time and cost to the company.

    CONCLUSION

    After completing this module, I’ve gained the knowledge of data analytics in Financial Services, why it’s important, and most importantly how it helped to improve financial businesses. The gist is that analytics has taken over the task of discovering important insights from a huge amount of raw data in an automated manner with the help of technologies such as big data, which is impossible to be done by human beings.

    Business owners can now take advantage of these insights:

    1. to make data-driven decisions with proofs & evidences rather than intuition-based

    2. Grow business and discover new opportunities by quickly identify potential future markets & best areas for new investments

    3. Create more efficient and smarter orgs that predict and anticipate impacts economic, market, regulatory forces on business results. Use technology to analyse huge amount of data

    4. Manage risk and regulatory by ensuring completeness, accuracy and availability of data sources

    References

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    (n.d.). Retrieved from https://www.city-data.com/forum/work-employment/1372310-careers-financial-analytics-fin-analytics-vs.html
    (n.d.). Retrieved from PREDICTIVE: https://en.wikipedia.org/wiki/Predictive_analytics
    (n.d.). Retrieved from https://www.ibmbigdatahub.com/blog/analytics-banking-services
    (n.d.). Retrieved from https://en.wikipedia.org/wiki/Business_analytics
    (n.d.). Retrieved from analytics
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    (n.d.). Retrieved from https://www.mastersindatascience.org/resources/what-is-data-analytics/
    (n.d.). Retrieved from https://searchcio.techtarget.com/definition/Prescriptive-analytics
    (n.d.). Retrieved from https://www.klipfolio.com/blog/kpi-metric-measure
    (n.d.). Retrieved from http://www.coveringcredit.com/business_credit_articles/Credit_Risk_Analysis/art604.shtml
    (n.d.). Retrieved from https://www2.deloitte.com/content/dam/Deloitte/global/Documents/Financial-Services/gx-be-aers-fsi-credit-scoring.pdf
    bigdata. (n.d.). Retrieved from https://bigdata.cioreview.com/cxoinsight/role-of-data-analytics-in-financial-services-nid-24894-cid-15.html
    business case. (n.d.). Retrieved from https://www.business-case-analysis.com/financial-metrics.html
    centric. (n.d.). Retrieved from https://centricdigital.com/blog/big-data-visualization/how-can-predictive-analytics-improve-business/
    educba. (n.d.). Retrieved from https://www.educba.com/fraud-detection-analytics/
    educba. (n.d.). Retrieved from https://www.educba.com/financial-analytics/
    hipb2b. (n.d.). Retrieved from https://www.hipb2b.com/blog/metrics-analytics-and-kpis-whats-the-difference
    ibmbigdatahub. (n.d.). Retrieved from https://www.ibmbigdatahub.com/blog/analytics-banking-services
    loginworks. (n.d.). Retrieved from https://www.loginworks.com/blogs/top-10-benefits-of-data-analytics-for-business-houses/
    mafiadoc. (n.d.). Retrieved from https://mafiadoc.com/the-fight-against-bank-frauds-current-scenario-and-_5981650e1723ddeb563a0784.html
    masters. (n.d.). Retrieved from https://www.mastersindatascience.org/resources/what-is-data-analytics/
    predisys. (n.d.). Retrieved from https://www.predisys.com/Solutions/QualityDataAnalytics.aspx
    quantzig. (n.d.). Retrieved from https://www.quantzig.com/blog/benefits-financial-analytics-software

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