Impact of Data Science on the Future of the Finance Function

Roger Fried, Sr. Data Scientist at Teradata

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Let’s Talk About Hadoop, MapReduce, Spark, and…, and …

No, not for this talk. For this talk, let’s focus on the basics.

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The Future of Finance and Accounting• Various technologies employing Data Science are going to lead

to a hollowing out of the field where many tasks are much easier (executed perhaps by the CFO’s administrative assistant) and others will require more mathematical , technical, and people skills

• The changes will be so profound that they will blur traditional lines between Finance and Accounting and Data Science

Actually, the lines between all of the analytic functions will blur together with Data Science

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Examples of “applied Data Science”• Software will automatically create both your PowerPoint

presentations and the text for your earnings call

• Any employee may make sophisticated queries of your financial performance with natural language questions (think Google combined with Siri tied to all of your financial data with better answers)

• Applications will use “big data” (especially web crawling among regulatory filings) to predict yours - and your competitors’ performance

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Let’s talk about how a simple journey can change your perspective

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The problem with averages (and Statistics)

Technically, this is an illustration of the Central Limit Theorem, but it works in this argument too.

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Aggregates (averages, sums, minimums, maximums) all have the same effect

While your organization is not a fish, there is a complex story to be understood that is only visible with the detail.

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Think of the financial statement and financial ratios as that poor fish, in a shot glass, after the blender

The ability to drill down into the detail with BI tools does not really address this issue even if it gives you a false confidence that you have a handle on the detail.

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We need the detail: time series decomposition

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We need the detail: automatic categorization

Image of a Decision Tree, one of the most intuitive of the categorization techniques

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We need the detail: automatic clustering into comparable units

Combination of dimensionality reduction (PCA) and SVM and GMMclustering techniques

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Human classification = Bias

The science of research design invests immense resources into dealing with this problem and yet scientific research is still plagued with this problem. Accounting barely recognizes the problem.

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Accounting is pretty much the process of pouring bias into transaction data•Account•Department•Cost Center•Fiscal Period•Fund Account•Asset Category

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Accounting categories limit analysis because they are… • Static and out of date•Driven by accounting system needs rather than analysis needs•Created by a limited number of people with particular, possibly incompatible viewpoints •Unified and standard• Biased by preconceived (or ill-conceived) notions

This is not to say that we have to stop the process of assigning accounting categories, but rather that we cannot limit ourselves to them during analysis

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Techniques for analyzing all the data• i.e., Econometrics, Time Series

Decomposition• e.g., ARIMA models, SAX + Shapelets…

Forecasting•i.e., Unsupervised learning•e.g., KMeans, KNN, Hierarchical Clustering…Clustering•i.e., Supervised learning•e.g., Naïve Bayes, Decision Trees, GMM…Categorization•i.e., Feature selection and extraction•Principal Component Analysis, SVD, FA…

Dimensionality Reduction

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Datamart / Enterprise Data

Warehouse

Accounting Transactions

Budgeting System

Inventory System

Asset Management

SystemEmployee

Hierarchy, H/R & T&E DataCustomer

Relationship Management

Integrating data for a more complete picture

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•Needs to be at the multi-year transactional level – Aggregation is for reporting, not analysis– Detail is for analysis

• Start with, for example, 100 columns of data– Scale and bin numeric columns in multiple manners and arrive at

250 columns of data– Create alternative hierarchies and distance calculations for date

and categorical data to arrive at 500 columns of data• Data transformation may involve a large number of steps

which will be repeated many times during analysis

This data can be very awkward to use

We need the right tools and processes

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Use cases for “core Data Science”• Automatic categorization• (Useful) Outlier identification and evaluation• Fraud and embezzlement identification• Performance evaluation•Organizational reengineering• Improved forecasting (Self learning and granular)• Radically new visualization of the enterprise

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Thank you for your time today

Graph analysis of fund flows among and within companies

Roger.Fried@Teradata.comImage produced with Teradata Aster