Let ECONOFINANCE

Turn your Risks into
Opportunities!

ECONOFINANCE provides innovative risk management solutions to cater to the present and future needs of the banking & financial industry

Our Company

ECONOFINANCE is a risk management consultancy, dedicated to providing risk management services to Islamic banks, conventional banks, microfinance banks and other financial institutions, at affordable prices. We have run simulations and have also developed digital banking apps.

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We have a team of highly educated, experienced and dedicated professionals who have developed digital banking apps on parametric approaches for risk management. We use Excel, Java and Python technologies among other approaches like linear programming. We have used Value at Risk Approach, Parametric and Monte Carlo simulation methods along with the latest AI technology for the expected credit loss approach, to be used for credit and market risk.

introduction to ifrs 9

IFRS 9 is an International Financial Reporting Standard published by the International Accounting Standards Board. It addresses accounting for financial instruments. It contains three main topics IFRS 9 Financial Instruments, issued on 24 July 2014, is the IASB’s replacement of IAS 39 Financial Instruments: Recognition and Measurement. The Standard includes requirements for recognition and measurement, impairment, de-recognition and general hedge accounting.

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Our Team

Abdur Razzaq Shahid

Founding Director

M.A. Economics (York University, Canada), M.A. Economics (PU), M.A. English Literature (PU) 17 years of banking experience in leading banks, financial and economic research-based institutions like PIDE, HBL, IDBP, World Bank, ADB, etc. 18 years of teaching experience at leading universities and educational institutions including IBA, PU, IMS, UCP, FAST, etc.

Shahid Hameed

Chief Executive Officer

M.A. Economics, University of Karachi (1974). Over 45 years of experience as an Economist/Development Banker with prestigious institutions like PIDE and PLHC (retired as an EVP) and a Visiting Faculty at several leading universities. CEO, ECONOFINANCE

Dr. Ferhana Ahmad

Expert

DPhil (Oxon) PhD course: Mathematical Modelling (new)(PhD Finance and Operations Management streams) MS Financial Management course: Derivatives and Fixed Income Assistant Professor, Mathematical and Computational Finance, Suleman Dawood School of Business, LUMS.

Dr. Sajid Mehmood

Expert

PhD from the Department of Computer Science and Engineering, UET, Lahore in 2014. Associate Professor and the Chairperson, Department of Informatics & Systems, UMT.

Dr. Ammar A. Raja

Expert

PhD (Finance), London School of Economics Director, CETA School of Business and Economics, University of Management & Technology

Mustafa Abdur Razzaq

Co-founder & Expert

M.A. Economics (PU) 15 years of banking experience with Prime Bank, ABN AMRO, RBS and Faysal Bank. Served as Business Analyst of Trade Finance application. Serving as SQA Analyst for CRM application

major benefits of our software

  • Provides a comprehensive end-to-end solution for the estimation of ECL provisions under current IFRS 9 requirements.
  • Supports all major IT platforms and browsers.
  • Easy to use and requires minimum data input from its users.
  • Equally capable of estimating ECL for large input data stream, as well as for a minimum of single quarter’s input data.
  • Comes with free on-site users’ training and very strong 4-tier technical support.
  • Provides free software updates as and when IFRS amends or refines its methodology
Contact Us

ECONOFINANCE

MON–FRI 9AM–6PM

9/110, Main Gulberg Road,
Gulberg 2, Lahore, Pakistan

contact@econofinance.net

Our Representative in Dubai:

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2026

ECONOFINANCE

All rights reserved.

Our major solutions include:

  • ECL Insight Pro (It delivers deep insights into Expected Credit Loss (ECL) modeling, particularly signaling intelligence, machine learning, analytics, and IFRS-9 alignment)
  • CrediGuard ECL (A robust and advanced IFRS platform for ECL modeling and reporting)
  • ECL AutoSoft Pro (Fully automated ECL solution based on mathematical and statistical models on Python and Excel, for small financial institutions with limited budgets)

 
Salient Features of the Software

  • ECL Insight Pro is a complete IFRS 9 ECL automation engine.
  • Handle complex PD/LGD/EAD modeling with Random Forest and explainable AI.
  • Automate monthly credit-risk pipelines and generate instant dashboards.
  • Scenario weighting, staging, and aggregation—done in seconds.
  • Built for banks seeking accuracy, speed, and compliance.
  • CrediGuard ECL is a robust IFRS 9 platform for Expected Credit Loss modeling and reporting.
  • It delivers advanced PD, LGD, and EAD analytics with scenario-based ECL calculations.
  • Automated workflows and explainable models ensure accuracy and regulatory compliance.
  • Built for banks and financial institutions seeking speed, control, and confidence.

 

Some of our products for Islamic banks include the following:

  • Credit Risk
  • ECL (IFRS 9) - Risk Management perspective – Islamic banks
  • Digital Banking – Mobile Apps
  • Linear programming software for capital planning

Some of our products for conventional banks are:

  • Market Risk
  • ECL (IFRS 9) - Conventional Banks
  • Consumer Finance
  • Credit Risk

In addition, we also provide the following:

  • Cloud apps
  • Third Party Integration
  • Functionality to import and export data from and to worksheet to databases.
  • In addition to marketing our risk management softwares, we are particularly focusing on our software for estimating expected credit losses.
  • To install our various products/software at various banks and financial institutions, we have an implementation team, which would always be available.
  • Initially, we prefer to send an introductory letter to all our prospective clients along with some information and related material to familiarize them regarding our company and its various products.
  • Once our clients seem interested in our work, we arrange a detailed demonstration based on our products and services on Zoom, at the convenience of our prospective clients.
  • Later, we send a comprehensive business proposal to our clients based on their requirements with cost estimates and implementation time frame.

IFRS 9 Financial Instruments, issued on 24 July 2014, is the IASB’s replacement of IAS 39 Financial Instruments: Recognition and Measurement. The Standard includes requirements for recognition and measurement, impairment, de-recognition and general hedge accounting. The IASB completed its project to replace IAS 39 in phases, adding to the standard as it completed each phase.

The version of IFRS 9 issued in 2014 supersedes all previous versions and is mandatorily effective for periods beginning on or after 1 January 2018 with early adoption permitted (subject to local endorsement requirements). For a limited period, previous versions of IFRS 9 may be adopted early if not already done so provided the relevant date of initial application is before 1 February 2015.

Calculation of Expected Credit Loss (ECL)

The basic ECL formula for any asset is ECL = EAD x PD x LGD. This must be further refined based on the specific requirements of each company, the approach taken for each asset, factors of sensitivity and discounting factors based on the estimated life of assets as required.

Default probability, or probability of default (PD), is the likelihood that a borrower will fail to pay back a debt. For individuals, a FICO score is used to gauge credit risk.

The value of LGD can simply be calculated as the actual total losses observed only on contracts that defaulted a long time ago.

PD used for IFRS 9 should be ‘point in time’ (‘PIT’) probabilities (that is, probability of default in current economic conditions) and do not contain adjustment for prudence.

Calculation example: An entity has an unsecured receivable of EUR 100 million owed by a customer with a remaining term of one year, a one-year probability of default of 1% and a loss given default of 50%. This results in expected credit losses of EUR 0.5 million (ECL = 100 * 1% * 0.5).

IFRS 9 requires recognition of impairment losses on a forward-looking basis, which means that impairment loss is recognized before the occurrence of any credit event. These impairment losses are referred to as expected credit losses (‘ECL’).

ECL Prediction Using Multiple Linear Regression

We have also developed a software, using a Multiple Linear Regression Model to predict Expected Credit Loss (ECL), which is a forward-looking measure that estimates potential financial losses due to credit defaults. Multiple Linear Regression is applied to predict ECL by leveraging historical data and key financial predictors. The model provides a step-by-step analysis, interprets results, and highlights the model’s business implications.

The business value of the model includes the following:

  • Improved risk management due to accurate ECL predictions and enhanced compliance with financial regulations
  • Cost efficiency is due to reduction in manual efforts in risk assessment and increasing precision in financial decision making.
  • It increases investor confidence by providing a transparent, data-driven approach to credit risk management and building trust in institutional resilience.

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