 Housing Finance Companies are indirect lenders and lack access to stable funding from CASA deposits thereby making them more vulnerable to market dynamics . . Liquidity is an institution’s ability to meet its liabilities either by borrowing or converting assets. Asset Liability Management (ALM) addresses the risk faced by a bank due to a mismatch between assets and liabilities due to liquidity. Apart from liquidity, a bank may also have a mismatch due to changes in interest rates, as banks typically tend to borrow short term (fixed or floating) and lend long term (fixed or floating). Over the last few years the financial markets worldwide have witnessed wide ranging changes at fast pace. Intense competition for business involving both the assets and liabilities, together with increasing volatility in the domestic interest rates as well as foreign exchange rates, has brought pressure on the management of banks to maintain a good balance among spreads, profitability and long-term viability. These pressures call for structured and comprehensive measures and not just ad hoc actions. Banks must base their business decisions on a dynamic and integrated risk management system and process, driven by a comprehensive ALM strategy. Like any other financial institution, Housing Finance Companies (HFCs) are exposed to several major risks in the course of their business – credit risk, interest rate risk, foreign exchange risk, equity and commodity price risk, liquidity risk and operational risks. Majority of HFCs are indirect lenders and lack access to stable funding from CASA deposits thereby making them more vulnerable to dynamics of the market. HFCs create multiple fund sources. Borrow money from banks – term loans – and re-lend them, thereby creating long term assets with short-to-medium term liability. When the term loan matures it may be re-priced thereby creating a potential ALM mismatch. Raise external commercial borrowings in USD or any other currency, potentially creating foreign exchange risk and subsequent ALM mismatch. Though, technically, these are secured loans, the collateral is not easily liquefiable, thereby again creating potential for ALM mismatch that may arise from loan defaults and worse in case of default of loans given for under-construction property. Rising competition also brings threat to loan assets in the form of balance transfers. ALM directly or indirectly impacts key ratios for an HFC.  ALM process rests on three pillars – ALM Information System, Management Information System, and Information Availability, Accuracy, Adequacy and Expediency, which in turn are functions of people process and technology. Although HFCs do file ALM status periodically, as per regulatory norms, the calculation is based on a deterministic approach. Stochastic approaches have not found their way into the system. Interest Rate Risk lies at the core of ALM and is studied from two perspectives: economic value and earnings. Sensitivity of Economic Value of Equity (EVE) arising from market shifts helps observe Fair Value Volatility. Modelling this volatility of earnings under different scenarios, using advanced simulation techniques, is essential to study the impact on the future balance sheet and income. Scenarios Analysis must then be fundamentally extended to examine, manage and stress the structural liquidity gap, Liquidity Coverage Ratio (LCR), Net Stable Funding Ratio (NSFR), and the liquidity survival horizon. Answer lies in an integrated risk management framework that allows for strategic planning, a state one arrives when the budgeting process tactics and ALM are in complete control.  Automating the ALM process of data collection and MIS may help it become more real time business intelligence solution. In addition to staying on top of market trends, HFCs need to make conscious efforts to develop, design and deploy stochastic models, leveraging macroeconomic data, indices, market data and other alternative data.
Leveraging the power and speed of big data, advanced analytics and intuitive yet powerful visualizations these stochastic approaches ideally should be able to: • Analyze and predict the impact of market dynamics on assets and liability • Able to differentiate the impact leading to prescriptive corrective measures • Adapt and evolve with trends, both technological and business • Continuously improve and learn Developing such capabilities would provide an edge to the organization in this hyper competitive business of borrowing and lending. Stochastic approach would help HFCs mitigate ALM risks, efficiently deploy capital and execute wiser hedging strategies. 
Demand for credit is high yet credible information to appraise borrower’s financially is deficient. While the MSME business models are evolving, the lending frameworks to assess their financial needs remain traditional.
The rise of diversified economies and end of monopolies has led to the ascent of micro, small & medium enterprises (MSME) across the world. Both developing and developed nations have a sizeable strength of MSME businesses that form the backbone of their economies. Recognizing the potential for job creation and economic growth, several countries have developed financial access and support programs targeted specifically at MSMEs. However, living through the global recessionary outlook the changing dynamics have created a new challenge for the lending industry. Credit crunch and poor credit ratings are contributing to the problem of selective supply. Demand for credit is high yet credible information to appraise borrower’s financially is deficient. While the MSME business models are evolving, the lending frameworks to assess their financial needs remain traditional.
Running through select global SME facts & stats, research suggests that firms with fewer than 500 employees, are the core strength of the U.S. economy. They make up 99 percent of all firms, employ over 50 percent of private sector employees, and generate 65 percent of net new private sector jobs. SMEs account for over half of the U.S. non-farm GDP, and represent 98 percent of all the U.S. exporters and 34 percent of the U.S. export revenues. In the EU, SMEs represent 99% of all businesses. It provides employment to a large number of people and also makes up a significant portion of the nation's GDP. Unnoticed until recently, the policy makers and ministers have started paying attention to this sector and have slowly started to realize the true potential it holds. SMEs tend to be more vulnerable in times of crisis because it is difficult for them to downsize, they are not economically diverse and most importantly they have fewer financing options. Evidence exists to show that SMEs in most countries are confronted with a clear downturn in demand for goods and services if not a demand slump in the fourth quarter of 2008. There is little transparency regarding the financial conditions of SMEs, therefore, banks hesitate to give loans to small scale units. Significant proportion of loans given to small enterprises in the past have compounded the problem of non-performing assets (NPAs) for banks. Unless fairly detailed information on small firms is available, banks hesitate to take the risk and may prefer to lend to relatively larger firms to comply with regulation, thus leaving smaller firms significantly constrained for capital. Improving the quality of financial information is an important requirement for enhancing the flow of funds to the SME sector, as the quality of information also influences decisions on loan finance. Borrower? or Lender? . . Challenges remain on both sides . .
“We have a huge work-force dedicated to this segment, however loan sanction rates are still very low. Many applications are rejected as the borrowers lack credible documents, collaterals, cash-flows and relevant credit history. For small borrowers it is hard to take credit risks without proper information.”
Meanwhile, a few hundred kms away, Dr. Anuradha is running a Maternity clinic at her ancestral town named Hasanpur in U.P (India). She wants to expand the facility to have in-patient and critical care services. Dr. Anuradha says – “Most serious cases are referred to bigger multi-speciality centres in Delhi NCR. There is no other obstetrics centre with critical care facility in the area.” She believes her business currently has a huge lost sales component. For the planned expansion she needs INR 2 Crore (INR 20 million). Though she would like to borrow money for expansion, banks have historically declined her application based on the current size of balance sheet. Meteoric rise of MSME Sector in India
As this segment grows, so does its need for credit. As on March 2017, credit to MSMEs in the formal sector stood at INR 16 trillion and is expected to grow at a rate of 12% to 14%. The unmet credit demand in the MSME segment was estimated to be nearly INR 25 trillion in the FY 2017. Noticeably banks in India are not provisioned with specific targets for lending to MSMEs. Bank loans given to the micro and small enterprises is part of the priority sector lending initiative - Indian banks are required to achieve a target of 40% of adjusted net bank credit to the priority sector, while foreign banks have a target of 32% exposure to the priority sector.
Centre for Civil Society study suggests that access to credit is one of the top challenges of a MSME business. Debt is the primary source of finance for most MSME businesses. However, most of them struggle to qualify for loans due to lack of collateral and positive balance sheets. According to IFC, the dominant source of debt for MSME industry is still the informal sector which has much higher interest rates and poses a huge growth barrier for the industry. While the lending industry is aware of growing MSME credit demand, multiple challenges have hindered their aspirations to tap into this opportunity. Lenders rely on balance sheets, P&L statements, income tax certificates and collateral. Many a times, this information lacks transparency and credibility. Moreover, this information set is not a direct reflection of future success potential. There is no structured or automated approach to conduct additional analysis on MSME’s future performance. Operating within this traditional credit appraisal framework it is hard for lending institutions to make risk appetite based intelligent credit decisions. Disruption In The Lending Framework
Today, neural network driven Cognitive Agents adjudicate customer character, payment capacity, collateral value and lending conditions by intensively studying and comparing numerous factors - a Big Data Initiative - in real-time to underwrite and continue to enhance intelligence for advanced strategies on Risk Appetite Based Portfolio Management and Customer Acquisition.
In these disrupting times, it is pertinent that lending institutions adopt newer credit appraisal frameworks using alternate data and information sets. Big Data technology can enable lenders to consider non-obvious information which is vast in comparison to the traditional assessment methods. Leveraging Big Data and deriving credible information with algorithmic science can indicate future success potential of an MSME business. Developing approaches for data aggregation and analysis to study the potential of Big Data and its relation to the small business cash flow, let's explore the possibilities for the Healthcare MSME sector in India. Developing A Scoring Model For The Healthcare MSME
Alternate Data Sources | Augmenting Credit Decision Making With The Help Of Big Data
Healthcare MSME industry in India is burgeoning with the rise of private sector. Private sector accounts for 82% of outpatient visits and 58% of inpatient expenditure. Within the private sector infra, small hospitals or standalone centres play a pivotal role. As per NABH (National Accreditation Board for Hospital) – which has defined a new category called Small Healthcare Organizations (SHCO) – “50,000 Health care organizations are functioning in our country out of which significant number falls under the SHCO category with < 50 beds”. Healthcare MSME sector is also facing a credit challenge, especially in tier II, III cities and rural towns. Services are fast aggregating to metropolitan areas depriving people in towns and rural areas from healthcare services.
Developing Machine Learning and Neural Network driven statistically prudent models that are based on big-data micro-patterns to adjudicate customer character, future revenue potential and paying capacity is the key differentiation. Harnessing knowledge and experiences from our Healthcare and Lifesciences industry vertical, we at Profisor, undertook a research initiative to device a sector focused lending framework. The objective of our research engagement was to devise a model, to augment traditional assessment scores with big-data based intelligence and to enable lending institutions make more informed decisions. Given the variegated nature of businesses across healthcare MSME industry we segmented the businesses into Business to Customer (B2C) and Business to Business categories (B2B). Machine learning models were developed for both B2C and B2B segments. 
Healthcare MSME Segmentation | Assessment Levers For Identifying Alternate Information Sources
Numerous factors impacting the success potential of a MSME business – including financial, social, political, demographic, micro and macro-economic variables were analysed and compared in real time to strengthen the agility of the lending model.
NABH Accredited Healthcare Providers
Demographic Parameters : Demand Fueled By Changing Per Capita GDP
Designing The Next-gen Credit Appraisal Framework
Concluding our research hypothesis with astounding and insightful results, we arrived at a ratings mechanism. Not all data and data sources proved relevant. Macroeconomic factors, along with select social and demographic factors, turned out to be a better indicator of the MSMEs business success.
Devising a ratings engine is living the art and a science of the lending business. Select set of challenges one faces in this journey include:
Lending models are bound to evolve and with Big Data on our side the possibilities are amplifying every day. Not one hypothesis will stay the longer course. Methods and or approaches for model design and validation must be of higher importance. With time the impact of changing market conditions and borrower’s repayment behaviours must be analysed to study the maturity of new-age assessment models. Although the math will continue to advance, lending machinery should distinguish on methods beyond application of big data, for instance the Cash Flow Analysis of borrower by their business type (Listed Company, Private Company, Partnership, Proprietorship and others); Fraud Analytics to eliminate “Cook Book” scenarios; and Social Media Reputation scores for consumer facing businesses. Innovation necessitates higher business risk appetite . . be the first or await a new benchmark scoring mechanism from a startup, choice is yours to make . .  Developing nations are battling the financial inclusion agenda . . un-banked, un-aware and digitally less savvy members of the society, the needful potentials, remain under-served and or un-served . . . . Innovators are recognizing and toiling at containing the power of technological advancements. Leading the pack are trendsetters like Lenddo, who have developed, differentiated underwriting models by aggregating and absorbing big data. Many a business models have emerged across markets, over the last decade. While each story is unique the capabilities are a harmonious blend of the art and science of business, led with next-gen technologies - namely big data, data engineering, statistical science, machine [deep] learning and artificial intelligence. This article shares with you an independent view of leveraging technology to outsmart your competition before your business goes obsolete. “Disruption”, from the life of a world-tea plantation worker . . Imagine the life of a lady named Manali, working on a tea plantation in Darjeeling She harvests tea leaves for a living. For manufacture of ‘high-quality’ tea, the tea shoot at the precise maturity should comprise of an unfurled bud with two or three soft leaves. Though Manali is an expert at what she does, the compensation is in cash, undocumented and just enough to get her by. Manali’s livelihood maybe disrupted by automatic leaf harvesting and has the potential of robbing her of her livelihood. We are all living in an age where we get to experience disruptive technologies each day. Innovations are taking place every minute, every hour. Most markets are facing ‘disruption’. For example, Bitcoin disrupting banking with their block chain technology, Uber disrupting urban commute with their ride aggregation technology. While one technology is out to destroy Manali’s current livelihood, another might offer her alternative livelihood. Creating alternative livelihoods may require capital. Does Manali have access to capital or credit? Would banks be able to check her credit worthiness & extend credit? Who appraises Manali and millions like her whose credit worthiness cannot be judged by existing systems? Organization like Lenddo help bridge this gap. We do not promote Lenddo. Our interest is in actively studying emerging business trends to bring credible and independent opinions to our clients, to help them transform their business models. In emerging economies, like India, people have cash but no credit, to have credit one needs credit history, and to have a credit history one needs credit, this is sort of a ‘Chicken-and-Egg’ situation. A situation that has puzzled both banking and non-banking financial services institutions. While the ‘Chicken and Egg’ conundrum survives its last breath, Lenddo is out to settle the ‘Credit-Credit History’ problem. Lenddo has a goal of improving ‘Financial Inclusion’ for at least a billion people in developing countries around the world. Who is Lenddo? Lenddo co-founded by Jeffery Stewart and Richard Eldridge in the year 2011, has operations in Philippines, Colombia, Mexico, and in India. Lenddo leverages alternate data like social media activities, online activity and smart phone data to derive an individual level Credit Score. It analyzes these data points, with the help of Machine Learning capabilities, to determine a ‘willingness to pay’ or a ‘promise to pay’ score. Lenddo score ranges from 0 to 1000, higher the score, the lesser the probability of loan default. This Lenddo score can be used as an augmentation strategy prudently integrating it with traditional credit appraisal scores. The disrupter does this by sending the applicant a permission request on his or her cell phone and once the applicant gives their consent, then the Lenddo algorithm scans recipients phone for data points and derives a credit score in less than 3 minutes. With Lenddo’s help, financial institutions have reduced fraud by 20%, loan approval rates have gone up 15% and achieved an accuracy of more than 99.3%. All these figures are very impressive. Lenddo has a huge potential for playing a big part in unleashing the true capabilities of Micro Small Medium Enterprises (MSMEs) in developing countries by providing a non-traditional platform for credit appraisal. . . returning to Manali's story . . Although Manali works as a leaf harvester she has an amazing foresight. Manali can recognize and understand trends and changes in the industry that may occur. All this is possible because she has access to the internet on her cell phone and she enjoys watching TED talks. Manali always had a dream of owning and operating a small café and living in a tourist destination is already a plus. Connecting with her phone and seeking help from Lenddo she can approach banks for a small business loan and realize her dream of not just owning a café, but also her financial freedom.
Although a small story, but many such stories await to be realized. With due government initiatives, oversight and active participation from Banking and Financial Services institutions, platforms like Lenddo can genuinely help the needful and the overall economy of countries, like India, with an innovative and highly accurate Credit Appraisal System. Indian Railway Catering & Tourism Corporation's Flexi-Fares strategy jolts passenger occupancy and premium trains revenues – can dynamic pricing engines solve the revenue optimization challenge?
25th November 2016, Profisor Team makes a day trip from Delhi (The Capital City) to Ludhiana (Major City in North Western State of Punjab). An early start to the day . . all preparations done for a two-fold agenda 1. Conduct regional-level research for analysing a political party’s penetration using social media data and 2. Meet Professor Doc. K. S. Mann, Dean of Student Training & Placement Cell and Heads of IT Department at the Guru Nanak Dev Engineering College in Ludhiana to establishing an internship program for graduate and master level students.
Week before this planned travel, we booked our train tickets for Shatabdi Express, a premium Indian train that runs during the day usually with two types of passenger coaches – An Executive Class and A Chair Car. Quite recently, on 9th of September 2016, Indian Railways Minister, Suresh Prabhu, had introduced Flexi-Fares (or Dynamic Fares) claiming to rake-in additional INR 500 Crores (USD 77 Million) by 31st March 2017. Much less to our surprise we paid 40% additional fare compared to our past travel experience and discovered a lower passenger occupancy in our coach. Is this the result of Flex-Fare System? Or Surge Pricing? The Uber Ola Debate! Upon further enquiry, from fellow travelers and train staff, we concluded with mixed, yet divided, responses. Passengers opined that the new fare system was negatively impacting the train occupancy rates, while the train staff appeared more optimistic hoping for improvements sooner than expected. As the calendar turns into December 2016, a big question looms on the face of Railways Minister, who has a strong bend towards technology, especially the use of social media for listening to train passengers and improving passenger experience. Does the Flexi-Fare system work? Owing to further decline in occupancy rates, on December 19, 2016, Railways tweaked the Flex-Fare Structure, offering 10% rebate on seats left vacant after chart preparation. Today we are in the month of March 2017 which marks the end of a financial year. Economic Times reports, “Flexi-Fare system in premier trains to be revised again: Indian Railways”. Railways earned about an additional INR 260 Crores from the Flexi-Fare System, nearly 48% short of their planned target of INR 500 Crores, and continued to face lower occupancy rates. What that means is fewer people travelled between 9th Sep 2016 and 28th March 2017, on trains operating with the Flexi-Fare System, paying a higher fare for the same journey, which in result contributed to the additional INR 260 Crore earnings. That’s quite a dent in the passenger’s pocket! Unfair! While that’s an opening statement for a big long news hour debate, let’s explore the dynamic pricing challenge for Indian Railways. Can Dynamic Pricing Engines Help Accomplish Higher Passenger Occupancy and Optimize Revenues? Here are some informational facts about Indian Railways and the Flex-Fare System or The Dynamic Pricing Engine.
How does the Indian Railways Flexi-Fare System Or Dynamic Fares Work?
What was the impact of Flex-Fare System on Revenues and Occupancy Rates?
Delhi to Mumbai travel is one of the most heavily booked journey and an equally relevant travel route for both domestic and international Airliners in India. A 10-week analysis of the route presents us with the following stats:
Delhi – Mumbai Rajdhani Express Train
Period Between 9th September 2016 to 19th November 2016
Delhi – Mumbai Rajdhani Express Train
Outside Festive Season Between 9th September 2016 to 15th October 2016
AKR, Mumbai – Hazarat Nizamuddin, Delhi Train
Period Between 9th September 2016 to 19th November 2016
AKR, Mumbai – Hazarat Nizamuddin, Delhi Train
Outside Festive Season Between 9th September 2016 to 15th October 2016
Against the principles of Dynamic Pricing, the current Indian Railways Flexi-Fare System changes fares based on Supply. Such an analysis must begin with a Segmentation exercise, a study of the Demand Supply factors and the total inventory of train seats pan-India, to arrive at trains and categories of seats that offer revenue enhancement opportunities. Opportunities that reflect operations research and data-oriented statistically prudent methods for successful implementation of Dynamic Pricing, Upselling, Resource Optimization, Meals Cost & Sales Optimization, Route-wise and Segment-wise Meals Personalization, Train Schedule Optimization (although politically driven . . ) . . and many more . . . .
Are You Flying High Enough? - Offers insight into how such studies are performed in the Airlines industry.
Data is rich. Technologies are emerging. In today’s times where Data Scientists are joining hands with business to solve computational problems, Indian Railways must make a genuine effort to evaluate all factors that contribute to the study of Dynamic Pricing of train passengers, and exercise caution when analysing the impact of fare changes on the travelers’ pockets.
Are there off-the-shelf products that may be deployed to achieve revenue or inventory cost optimization or should one develop these models in-house? Optimization in this day and age invites interest in Technology. Stakeholders across business look up to their Technology Teams for advise. Product Or Platform Or Service - questions continue to reel into discussions. Ownership, Governance, Security, Stability, Commercial Viability . . all aspects must be evaluated before planning a roadmap. Challenge does not end with answers to these questions. Organizations operate with differing models. Models that reflect their taste and reservations in Data and Technology Governance. While one set of businesses may feel comfortable in leveraging raw transactional data, others tend to live curated information with limited access to raw data. That brings us to our last leg of "Optimization Challenge" - The Final 100 Meters. Concluding on the role of Technology Architectures in our previous viewpoint "Investment In New-age Technology Architecture Prevents A Fall & A Fracture", we delve into the evaluation of Products or Solutions. Are there ready products that fit any environment and are capable of delivering optimization? Or should one build these solutions in-house with or without the help of advisors? Before we discuss these questions in greater detail, let's review the optimization problem and recognize the objective. Capability Readiness To Undertake The Revenue Optimization Challenge  Are there available product offerings? What are their capabilities?  Note: Other relevant tools and products exist but we limited our search to most prevalent products. Many active players in the market have developed Dynamic Pricing engines that are capable of studying general demand supply factors and then there are others that claim to study the end-customer. These products are most successful when the problem statement is clearly defined and there are no challenges with availability of data. Not one product operates in a capacity to match the business acumen or business problem. Data architecture designs must have the flexibility to deploy these products. Data cleansing and harmonization are other big questions to resolve. In essence, the Technology Architecture must be flexible to accommodate off-the-shelf products. Organizations must evaluate their longer term purpose when making this decision to invest in products or solutions. Often in the act of driving efficiencies, decisions lead to situations that do not serve the business purpose. Investment of time and effort should be towards the sole objectives of business and not to deviate focus and energies from customer and or business. Questions that must form part of the evaluation must include: 1. Is this the primary objective and a key focus of our business and our customers? 2. Do we need the technology build and the knowledge to reside within the organization? 3. Do we need the capabilities to be able to deliver optimization challenges in the future? 4. Do we have the knowledge to put together a transformation roadmap? 5. Are available products flexible, delivery ready, match our technology stack and capable of replicating our business acumen? Why do we need a boutique solution?
With that we reach the end of our four series viewpoint, a 400 meters relay, on dealing with the revenue or cost optimization challenge. Businesses are disruptive. Technologies are emerging. Data is our best friend if we acknowledge and respect. Investments in a product or a solution will define the future of our business and our customers. Seek Advise To Plan Wise.
Why Technology needs a special focus when aiming for revenue or inventory cost optimization?
Before we explain the role of technology, let’s remember the problem statement. Case in perspective is Revenue Optimization – a challenge to manage perishable inventor sales within a defined lifetime of the inventory to improve revenue. We discussed the imperatives of Factor Analysis and Statistical Models in our previous viewpoint – Statistics Can Be Majestic If The Factors Have Characteristic. Multiple factors, rising counts of data and studying multiple data relationship requires intense analysis. Make it a real-time problem and you need a robust compute-intensive solution, ready to deliver at speed, at scale and with highest precision at scale. One miscalculation or wrong choice of factor that contributes to the problem of revenue optimization can sink the ship.
Are You Flying High Enough? A use case that offers insight on how dynamic pricing helps optimize revenue.
Achieving revenue optimization through a technology based solution is an act where machines prescribe different price points for a product, based on a real-time analysis of all the factors that contribute to the demand and supply analysis of such product. Hence, technology architecture plays a significant role in delivering these accurate price prescriptions. Let’s break this further into a study of different aspects of technology.
Emerging technologies have far superior capabilities compared to the ones we currently operate – data consumption, data processing, data cleansing, parallel computing and analysis. Their viability, flexibility, strength and scalability must be adjudged before putting any ideas to practice.
Next up, the last 100 meters, Products Or Solutions, Seek Advise To Plan Wise "Why is it important to invest time and effort into the study of factor analysis and statistical Science when designing a dynamic pricing model?"
It is a race against time and tough competition to reach the finish line, first. Planning and preparation is essential. Breaking into the revenue optimization problem and complexities associated with perishable inventory management, those discussed in our first viewpoint – Make A Move Or Choose To Lose, we concluded with a set of pertinent questions that the business must answer when they decisively undertake a journey towards devising a dynamic pricing engine.
The next 100-meter relay readiness is about shaping a study of Factors Analysis and Statistics. Factors that represent commercials, competitors, customers, seasonality and the differentiated business model. Followed by, a prudent choice of statistical models, ones with evidential accuracy, that will drive price prescriptions for revenue optimization.
Judicious selection of data sources forms the fundamental basis for Factors Analysis and Statistics. Taking events data into perspective, by way of example, 26th January marks the Republic Day celebration in India. Parades showcasing India’s defence capability and its cultural and social heritage take place at Rajpath in the Nation’s Capital – Delhi. Amidst high security alerts, the Nation’s President, the Governing Machinery and a Chief Guest travel into Delhi to celebrate and pay their tributes. Necessary security measures enforcing preventive checks impact the state border, city and inter-state trains, metro rails, air traffic and road transport. A quick look at the week of 20th Jan to 26th Jan offers the following information:
Changing trends, otherwise unavailable in seasonality plans or pricing plans of airliners and hoteliers, can be converted into the business opportunities, using statistical algorithms.
Are You Flying High Enough? A use case that offers insight on how dynamic pricing helps optimize revenue.
Information on events is publicly available which could be aggregated to build an events repository. Then, events that have a strong correlation with booking curves must be shortlisted for a comprehensive study of demand trends. An equally relevant source of real time traffic information is also useful to understand the impact of no-shows on changing booking curves. Speaking of which, Google, Bing and Yahoo Map APIs (and soon Uber Maps) provide intelligent traffic data insights.
To conclude succinctly, statistical algorithms are only as good as the quality of data sources, business assumptions and the quantification techniques. While linear regression remains the favourable choice for arriving at the dynamic price range, for accomplishing revenue optimization, developing and implementing a statistical model and validating its decay rates requisites feature engineering capabilities. Real-time model operability for pricing decisions is most successful when technology infrastructure is architected to engender statistical modelling at its highest precision levels.
The buck doesn’t stop here or with the development and implementation of a statistical model once. Frequent examination of the changing characteristics of factors and validation of the statistical models for change in decay rates is essential to find the new optimized levels.
Next up, Investment In New-age Technology Architecture Prevents A Fall & A Fracture How must Financial Institutions Plan and prepare when charting a journey towards operational risk management?
People, Process and Technology, the Operations Ecosystem, is fast evolving, and posing new challenges and risks for the senior management. Time and again the robustness of an organization’s machinery is undergoing a test of its design, its capability maturity and its operating effectiveness. Recent and recurring market and industry risk events have re-emphasized the need for Indian Financial Institutions to recognize their failures resulting from sub-standard business practices and inadequate technology investments, and to position renewed energies into Operational Risk Management.
Highlights of few select events from the recent past are as follows:
In a previous article on this subject – Mind The Gap – we elaborated on the boundaries between operational risk with conduct risk in context to the changing regulatory landscape in UK. Understanding these risk boundaries is not a complex process but implementing an organization-wide methodology "consistently" is a significant task. Mature markets in the western economies continue to explore and develop scalable and sustainable models to embed a culture of risk management.
Financial Institutions in India have made varying degrees of effort and resource investments in developing policies, processes and controls, followed by periodic self-assessments of risks and controls. While a few have exercised rigor in performing comprehensive risk impact assessment studies, most others have bought themselves a ready reckoner, a checklist, that acts as a handbook to meet regulator’s expectations. Organizations who perceive this as a problem of non-compliance with policies and procedures continuously make additional investments in cultivating a culture of absolute compliance. However, quantification of effort and investment for exercising control over operations and realizing efficiencies remains a difficult challenge. Only a handful organizations have chosen the path of evaluating their investment to perform an integrated analysis of operational performance and risk management for greater good of their business, to drive efficiencies and to optimize costs. Reviewing the recent risk and regulatory events in India and the maturity of risk management practices followed by Indian Financial institutions, we recognize the need for defining essential capabilities that form part of an operational risk management framework. Let’s begin by examining the key elements of an operational risk management framework. 
Copyright © Profisor Services Pvt. Ltd. 2015. All rights reserved. This document is subject to contract and contains confidential and proprietary information.
"There are many pitfalls in the journey Towards effective operational risk management that will ‘Wreck Your Trek’ . . . . knowing them ahead of time will certainly help in planning for the rough phases and managing the challenges more confidently."
Where does one start? What are the key enablers of operational risk management? What is the role for technology? What are the necessary capabilities? How should one develop capabilities? How does one test the level of maturity? What mature practices from western economies offer ready learning? Answers to these and many related questions will offer insights into challenges, hurdles, which most financial institutions encounter in their efforts to embed an effective operational risk culture.
With that aim in mind we present you the typical journey, articulating in detail the best practices, success factors, potential issues, causes of inefficiencies and industry-wide challenges at every stage of the process - from ORM Enablers to Risk & Control Self Assessment (RCSA), Key Risk Indicator (KRI) & Risk Events to Stress Testing and to Capital Management. Insightful Journey Into Effective Operational Risk Management
Copyright © Profisor Services Pvt. Ltd. 2015. All rights reserved. This document is subject to contract and contains confidential and proprietary information.
While the above info-graph offers learning opportunity, in our experience, organizations always benefit from engaging with proficient advisors to determine an approach for robust capability development to effective operational risk management.
Remember, resources are scarce and any wasteful investments will adversely impact your operational losses! How dynamic pricing and inventory management models can help optimize revenue and perishable inventory management costs?
Numerous businesses, across industries, deal with perishable inventory in everyday operations . . be it goods, like fresh food, meat, chemicals, composite materials, blood products, medicines, surgical instruments, clothing, etc. or service based products, like flight and train seats, hotel rooms, concert tickets, etc. . . managing shelf-life, shelf and or storage space, time bound consumption or utilization, and time bound sales necessitates finite decision making to optimize revenue and inventory costs. This challenge spreads further to logistics and supply chain businesses who have a significant role to play in managing the perishable inventory lifecycle.
Over the years, industries and businesses have made significant investments in tools and technology that deliver streamlined processes, generate the much-needed data and information, and help in day-to-day decision making. With advancements in business, operations, transaction size, data size, data type and evolving relationship of data to transactions, the number of factors that form part of the problem statement continue to evolve and expand. Changing and evolving factors have made the decision-making process more complex and raised the demand for comprehensive analysis of multiple factors.
This is where statistics and science are joining hands with technology to produce prudent predictions and prescriptions, and to offer intelligence that drives revenue growth and cost optimization. Revenue Optimization challenge is one such study of several variables that can be categorized into commercial factors, competition factors, seasonal factors and customer preference factors. 
Before one begins to study these factors, an equally bigger challenge is to define and manage the life of perishable inventory. While the shelf life is well defined for most goods, it is the service based products that demand a perishable life definition.
By way of example, what is the perishable life of a flight seat or a hotel room? . . and then more complex questions that require additional analysis, like does the perishable life of a flight seat change due to seasonal factors? or does it differ based on flight routes or flying distance? These and many more questions that contribute to the challenge at hand. Refer the example below of a short haul A 380 flight from Sydney to Melbourne that has 371 economy seats. 
Are You Flying High Enough? A use case that offers insight on how dynamic pricing helps optimize revenue.
Appreciating the challenge, we comprehensively define the problem statement with the following questions:
Many early stage movers have undertaken this challenge either by way of research and development and or M&A investment activity to build or own essential new-age technology capabilities that can solve for such problems and many more. Are you preparing to match the pace or are you already in the race? Continue to watch this space as we offer more insights into factor analysis, statistical science, role of technology and available capable solutions that can help you match pace with innovative data science techniques. Next up, Statistics Can Be Majestic If The Factors Have Characteristic Investment in People is not a new subject, yet it is the most sought after business agenda for organizations across the globe. Why? Why now? Is it the global markets crisis? Unemployment or underemployment? Job cuts? Rising pressures to perform? Changing business landscapes? Alignment or re-alignment of organizational strategy? Changing People culture? Renewed focus on diversity at workplace? Or a strategic focus on People to make a positive impact on the ROI? . . You may find or name more reasons, the name of the [New] game is People Analytics. Let's examine some interesting yet contrasting views from across the globe that are summoning us for well calculated, sound, decisions on People at workplace. 
Not hearsay and not a revelation either - a recent study "Learning to Fly", from AON Hewitt in 2015, suggests ". . that the CHRO is a critical stakeholder in defining the strategy of a firm.". This re-affirms that an organization’s prime focus is on People at workplace – their role in business strategy and business performance. CHRO’s strategic agenda is next on the radar. We ask some indicative and leading questions that pave the roadmap for measuring success. Again, only if there are proven methodologies to quantify and measure success!
For long organizations have made decisions based on intelligent intuitions and often undertaken initiatives to replicate well surveyed best practices. Yet, today, there is a strongly felt need for making more conscious and weighted efforts to design and develop solutions around People – to scientifically study the overall impact of this machinery on organization’s ROI. From identification and selection, on-boarding, retention, capability development, talent management, employee engagement, collaboration, diversity, employee performance, team performance, project performance to business performance – organizations are looking at Analytics to pave the way for making more sound decisions.
Wait, has anyone treaded into this vast domain of People Analytics? Are there any sophisticated, tried and tested (proven) models that may be contextualized and utilized? Not a comprehensive list, but here’s a quick highlight of select industry practices.
Does it mean that there are well-established and prevalent People Analytics practices and or ready tools to deploy into business? No! Let’s take a look at the global trends ~ Deloitte University Press – Global Human Capital Trends 2015 Report. There’s dearth of talent across industries and capability gaps are on the up-rise.  ‘Culture & Engagement’ is one of the most significant challenge across industries and regions. 
All unadulterated and glaring gaps are in our faces. Let’s face the music! Is there one specific reason that may reflect on these issues? No! There are numerous challenges and hurdles, some technical and others that relate to the level of acceptance, for the lack of use of Analytics around People at workplace. You may find these relevant to your business and operations context. Technical Challenges:
Other Challenges:
Where does one start? What should be the approach? Are there ready products in the market? Are products the way forward or should one undertake a solutions route? Can we leverage our existing business and operations capabilities? Do we need to upskill our People? Does that mean additional costs? Can we establish a case for investment into People Analytics? How does one embark on this journey? We recommend a tailored approach, leveraging your existing in-house business and operations potential, towards the development of comprehensive People Analytics capabilities.
We invite more questions and enquiries on the way forward, approaches and the design of a road-map towards intelligent use of organizational data to establish robust People Analytics capabilities. Continue to watch this space for more. You may write to us on people.analytics@profisor.com. Credits: AON Hewitt "Learning to Fly" Study 2015, Deloitte Global HR Trends 2015, Deloitte Millennials Survey 2016 & HBR.
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