Value at Risk

Market risk refers to the risk of losses in an organisations trading book due to changes in equity prices, interest rates, credit spreads, foreign-exchange rates, commodity prices, and other indicators whose values are set in a public market. To manage market risk, organizations deploy a number of highly sophisticated mathematical and statistical techniques.… Continue reading

Finalytica Jan-2018

A quick preview of developments in Analytics and AI space in India in 2017

  • Amazon Web Services adds AI to cloud by adding services such as image analysis, visual search, speech recognition among others
  • Google is upping its stake in India big time on AI and ML with Cloud India Region by announcing setting up its first cloud region in Mumbai within this year.
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Fund Transfer Pricing

What is a Fund Transfer Pricing System and why is it needed?

A Funds transfer pricing (FTP) is the process through which banks

and other financial institutes allocate their earnings to the various lines of businesses in which they are engaged.Continue reading

Happenings in AI, Analytics & Fintech world: December 2017

  1. Stock Exchanges in India are exploring the opportunity to venture beyond the current businesses to create data repository services, Artificial Intelligence or a big data analytics eco-system. Last week, the Bombay Stock Exchange and the National Stock Exchange sought Securities and Exchange Board of India (SEBI)’s permission to form a separate entity to take on these businesses.
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Primitives for AI

Let’s say I want to build a financial market prediction model based on news flow. The current way to do this is:

  • Collect lot of news articles
  • Collect financial market movement data
  • Use machine learning models (black-box or otherwise) to predict markets from news

This is not a scalable model of operation for building AI/ML solutions.

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Trending data-driven consulting

‘In God we trust, all other must bring data’ said late William Edward Deming – the famous  American statistician and professor. Gut based decision making is out, subjectivity is out, generic experience based decisions are out – Data driven decisions are in.… Continue reading

Insightful Decision-making


Many businesses use data insights to support their decisions instead of driving their actions. After all, data is only valuable if you can translate it into actionable insights. Your organization might be sitting on the world’s largest data pile, but it is useless unless you have the means to translate it into insights that drives your business.… Continue reading

Out-of-core Analytics using MonetDB


Most often than not, the amount of data one needs to analyse will fit in a hard disc. It may not fit in memory though. That’s where databases trump over in-memory solutions like R or Pandas (Python) for analytics. For sometime we have been arguing that column stores/columnar databases will be ideal for analytics (… Continue reading

Columnar DBs: Future of Structured Data


Contrary to popular perception, structured data analytics still forms a big potion of overall analytics/big data activity. Although unstructured data (text, data with variety etc.) is gaining more and more importance, structure data (transactions, numeric data etc.) still gives a lot of actionable insights for businesses.… Continue reading

Machine learning: what is and what is not

As elaborated earlier, Artificial Intelligence and machine learning are two different things. For a 19th century man even a simple calculating machine could constitute AI. But we all now agree that even more advanced computer algorithms are not machine learning but human intelligence codified into machine.

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Not So Big Data!!!


Imagine you are an organization sitting on a tons of transactional and other business data which is stored in structured database. Now, you have to analyse it on a daily basis to get vital insights about sales, customers, channels and many other business parameters, what will be your approach?… Continue reading

Why machine learning is worth the hype

Machine learning has seen significant hype in last one year. Applications range from chatbots to image searches to speech recognition to financial market prediction and more! Well the hype is all worth it. (Except may be for financial market prediction. Not may be, definitely.) ML will live upto its hype as:

It reduces friction in customer interactions.… Continue reading