case study related to credit and collection in the philippines

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Case Study: Optimising Credit and Collections Management at Edward Don

It is standard management practice to evaluate performance in key business areas against industry norms. At Edward Don and Company, we became quite concerned when our internal performance indicators revealed that we were flagging 66.2% of our active accounts as carrying a high credit risk. Subjectively, this number stood out as a very high scoring – and therefore very suspect – result. In practice, the high level of customer credit concerns were throwing up numerous road blocks in our core business, the distribution of food service equipment and supplies in the US.  

Reacting to what appeared to be a very poor customer base credit scenario resulted directly in several impacts on our operations:  

• What in fact turned out to be an excessive number of orders were being placed on ‘credit hold’. This reaction had a negative effect of requiring our collectors to spend an excessive amount of time on reviewing and releasing orders, and therefore resulted in less time for value-added activities. They were primarily spending time on the phone contacting customers for payment. About 98% of held orders had to be released manually, resulting in execution delays and increased processing costs. 
• The knock-on problems included growing frustration among our sales force, and an increasing risk that our customers were not optimising their sales potential.  

Clearly, it would have been imprudent simply to loosen our credit standards arbitrarily, which would run the risk of increasing payment delays and counterparty failures because of inadequate credit analysis. We needed to quickly find out which of our customers truly presented high levels of credit risk, so that we could then more confidently accelerate order processing for all the others. If we could achieve this, the vicious circle would swiftly transform into a virtuous circle, with accelerated payment performance, better satisfied customers, lower credit risk exposure, more efficient operations and enhanced business development opportunities for our sales force.  

The resolution of this issue was clearly a very urgent corporate priority, as it was significantly impacting our core business operations.   

The Base Situation

Edward Don and Company runs a decentralised 41 person set of credit and collections teams, distributed around our regional offices in Florida, New Jersey and Texas, and at the corporate headquarters in Illinois, US. The team includes 35 credit collectors/analysts, each of whom is responsible for about 1,200 accounts.  There are about 35,000 active accounts, growing at the rate of 150-200 new accounts per week. Our core business pattern consists of a high volume of relatively low value transactions.  

Our original approach to credit analysis involved the construction of generic scores, primarily based on information supplied by specialist credit bureaux. These scores were used to evaluate the creditworthiness of new accounts, so that credit lines and payment terms could be assigned to each account. They were also used to monitor the existing accounts, supplemented with the team’s analysis of published, financial reports plus some internally originated account performance data. The team also used the analysis to construct collection strategies where these were needed, in reaction to seriously late invoice payment performances. 

Generally, this describes a pretty standard methodology that is widely used in the credit and collection business, but it did not seem to be generating sufficiently accurate results for us. Therefore, the operation was becoming afflicted with bottlenecks, and was increasingly stressed, as I have described above. 

The team felt that the poor predictive quality we were experiencing with respect to accounts’ changing credit condition probably resulted primarily from a lack of accurate and timely input data. The underlying strategy then in place was, reasonably enough, quite conservative; but in practice, it was generally tending to assign far too many customers to an inappropriately high risk status, with the consequences I have outlined. One of the causes of this unsatisfactory situation was the basing of the analysis of some accounts on the ‘ship-to’ location, rather than focusing on the actual legal entity that truly reflected the risk. This approach will almost always lead to an underestimation of an account’s credit status.  

Paradoxically, other flaws in the analytical methodology were in some cases resulting in the underestimation of the true risk that was being carried by some other accounts. 

So we were operating in an unsatisfactory environment in terms of the timeliness and accuracy of our credit management process, and were therefore experiencing growing problem issues in collections, in risk exposure, and in related fields. We were also becoming increasingly sure that we were by no means using our professional credit and collections teams at anywhere near their full potential. Something had to be done.   

Initial Solution Identification and Validation

Our first analysis of the problem, and of potential solutions, strongly suggested that we could obtain much more valid and valuable results by using a statistical modelling process to support our credit and collections operation.  

We decided to invite SunGard to perform an initial analysis, and this they did in a complimentary validation exercise using a proven statistical model solution called AvantGard Predictive Metrics. Essentially, this model quantitatively predicts the chance that a given customer’s current good payments performance will deteriorate at some point over a time horizon of the next six months.  

In outline, the methodology for the validation was based on the statistical behavioural analysis of our historical accounts receivable (A/R) data. The end result showed the probability of each account becoming delinquent, expressed for example as the amount of cash at risk, which is a strikingly practical and vivid indicator of risk. Accordingly, we could see ahead into areas of potential future risk, by being able to evaluate the bottom line impact. 

As the predictions were derived based on the analysis of real data that we had supplied, the team felt the validation was based on an objective process that properly reflected our own ‘typical’ commercial operations.  

Among the innovations introduced into the analysis were proven variables such as accounts payment (A/R) histories, the impact of seasonal factors, the impact of changing economic conditions, and the trend performance of actual orders. Our understanding was that the introduction of such specific information would be effective and powerful factors in refining the analytical process. 

The validation exercise operated on a substantial volume of Edward Don and Company’s historical business data, representing 18 months’ history of actual receivables collection. The analysis reporting which we received was presented in clearly intelligible form, so that our teams could see which accounts out of our entire set of client accounts were the ones in reality, most likely to deteriorate in creditworthiness, and to potentially become delinquent.  

The key to our internal validation of SunGard’s model was the comparison of the forecasted results with the actual delinquencies that Edward Don and Company in fact experienced. The results showed a very high level of forecast accuracy and actual result correlation; so we were quite effectively satisfied that the AvantGard Predictive Metrics model had the proven potential to add real value to our credit and collection operations.  

Solution deployment   

Interestingly and most helpfully, the prospective solution validation exercise additionally provided a reliable template for rolling out the live solution in our country-wide operation, once we have received positive feedback from the initial stages of the project.  

The implementation team that was eventually put in place combined Edward Don and Company executives alongside SunGard’s AvantGard Predictive Metrics product specialists. 

The key operating result achieved upon launch was that the effective statistical analysis of each month of our data, required just one business day’s effort from now on. After this our teams were working with up-to-date, accurate and dependable data in support of their core professional duties.  

This substantial change led directly to several clearly quantifiable improvements being achieved in our credit analysis and management, order management and collection processes. And all of these advances had direct and positive impact on our core commercial activities.   

Tangible Benefits Achieved   

I have noted earlier that our prior credit and collection management processes had flagged 66.2% of our customer accounts to be high risk; and this apparent finding led directly to high volumes of orders being unnecessarily held up in our order processing workflow.  

In contrast, our new tools enable 80% of these orders to bypass the credit review system for immediate processing, immediately enhancing our cash flow performance, and improving our customers’ opinion of our order execution proficiency.  Most dramatically, today only 14.1% of our entire A/R portfolio is now classified to be high risk. 

Another immediate benefit achieved was that our credit collectors / analysts are now able to focus their professional attention where it is really needed, namely on those accounts which have been properly identified to truly merit high risk status. As our teams’ confidence in the quality of the new statistical analysis grew with increasing practical experience, the credit collectors / analysts found that they were able to dedicate much more of their time and energy onto those account situations in which they could make a real, measurable impact on improving our collection performance. 

It is also interesting to relate that the process is now more highly automated because of our confidence in the accuracy of the underlying analytical model. This means that many customer orders now flow through the system without requiring any kind of analyst intervention, and so are not impeded, as they used to be, by being held for research and resolution. This operational improvement additionally reduces our processing costs, enhances customer satisfaction levels, and liberates our sales force to concentrate on business development.  

A further, different type of benefit we have achieved is that, having identified the most high risk customers, we can now simply and accurately cut the relevant credit lines; and we can, conversely, increase lines to the more creditworthy customers identified in our new analytical environment.   

In practice, we have experienced a 25% increase in the volume of outgoing phone calls initiated by our credit and collections teams, reflecting their new capabilities to be more proactive in their account management duties, and in other priority operations.  

We can now set account management strategies that are directly linked to realistic risk assessment processes, both for the initial assignment of appropriate credit lines to new customers, and also for prioritising collection processes. The relevant information for each account is analysed and reported using a simple six-point risk grading system, and we can initiate research and report on any account, at any time. The reporting can also quantify the risk in cash terms, to broaden the value of the analysis for treasuries and other departmental units. 

Today’s still challenging economic and financial conditions mean that we cannot afford to be in any way complacent in our outlook toward credit and collection.  Every year, our team works with SunGard’s AvantGard team to revalidate and recalibrate the statistical models that support our operation, so that we can operate most effectively as external conditions change.  

Conclusions   

We are now confident that the Edward Don and Company’s credit and collections management process has been substantially optimised, through the deployment of the SunGard AvantGard Predictive Metrics statistical modelling solution. 

Perhaps the most telling current metric is the improvement in our days sales outstanding (DSO) performance by the highly significant amount of 5.3 days.  Where our former focus was concentrated on chasing aging receivables and on analysing customers’ credit terms, we now have the ability to predict and manage account credit deterioration more precisely and reliably. We can – and do – dedicate our professional resources to those accounts that really need it.  The benefits of these changes are reflected beyond our improved collection performance to more efficient account management, more satisfied customers, and a more productive sales team.  

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Microcredit in the Philippines

Jump to a section.

The initiative

The challenge

The public impact

• Stakeholder engagement Strong
• Political commitment Strong
• Public confidence Fair
• Clarity of objectives Good
• Strength of evidence Good
• Feasibility Good
• Management Strong
• Measurement Good
• Alignment Fair

Bibliography

The government responded with a programme of reforms - the MDP. It formed an important part of their overarching poverty reduction objectives and had three key components:

• Enhancing the enabling and regulatory environment of microfinance. This was intended to promote market efficiencies and outreach of services at competitive prices.
• Building viable microfinance institutions (MFIs) that could provide efficient and cost-effective retail delivery of services to the poor.
• Increasing financial literacy and consumer protection for the users of microfinance services.

The MDP was included in the Country Strategy Programme 2005-07, and implementation began in December 2005.

The MDP came to an end in December 2007, by which time:

• Outreach had increased from 1.3 million active borrowers in 2004 to 2.1 million by 2008. However, many of the poor were refused credit because they didn't have the capacity to repay a loan (a Catch-22 situation).
• There were positive rates of return on assets and equity. The MFIs demonstrated sustainable operations, with increases in financial education and consumer protection.
• The increase in loans to micro-enterprises and the new jobs created did result in some increase in household incomes. But the programme's relevance and effectiveness diminished because of its slow implementation.

In summary, the government had achieved a relatively sustainable and diverse, market-oriented microfinance sector with a wide outreach at competitive prices. However, it needed to speed up structural reforms and encourage MFIs to lend to poorer citizens.

Stakeholder engagement

The stakeholders were involved in all the stages of the MDP, and their participation formed one of its core conditions:

• The government collaborated with the banking community, cooperatives, NGOs, and the aid community in promoting microfinance as a poverty reduction tool and in achieving greater outreach and sustainability.
• The key factors that contributed to the success of the MDP included “the strong commitment of the government and support from stakeholders to develop the sector and achieve sustainable microfinance.
• The Asian Development Bank (ADB) provided significant loan capital and was a major stakeholder. This enabled the Philippines government to address systemic weaknesses in the microfinance sector.

Political commitment

This was a government initiative and, as a result, the project received strong political commitment:

• The MDP was a part of the government's Country Strategy Programme 2005-07 and was therefore closely aligned with its overall priorities.
• There was clear commitment from the government for the development of the microfinance sector and expansion of financial services to the poor, as embodied in the MDP.

Public confidence

Some efforts were made to consult the public, although not entirely successfully:

• Six public consultations were held, as this was a core condition of the ADB loan. However, the MDP server, envisaged as a platform for collecting public complaints, was not used.
• There were public objections to certain provisions within the MDP. This led to a careful staff assessment by the ADB and discussions with stakeholders to determine the impact of those provisions on the policy environment. They concluded that the provisions had no major negative impact on the development of the microfinance sector.

Clarity of objectives

The policy actions under the programme were all maintained and achieved:

• All the policy objectives were met. The envisaged outcome - of achieving a sustainable and diverse market-oriented microfinance sector with expanded outreach at competitive prices for the poor - was realised, although the last of these is qualified below.
• The programme design was an appropriate response to the identified sector issues and constraints - the weak supervisory capacity of the Cooperative Development Authority (CDA), weaknesses in the regulatory and supervisory framework, the limited capacity of MFIs to expand outreach, and the lack of financial literacy and consumer protection among poor and low-income households.

Strength of evidence

Feasibility

In defining the MDP, some aspects of resourcing and scheduling were given due consideration but there were weaknesses in other aspects:

• ADB fact-finding and appraisal missions were conducted to identify the sector issues and constraints and to develop and design the components for the MDP.
• The financing was given due consideration; a loan and a grant were approved early on in the MDP (a programme loan of $150 million accompanied by a technical assistance grant of $500,000 and a grant, financed by the Japan Fund for Poverty Reduction, of $900,000 for capacity development of savings and credit cooperatives and for strengthening the regulatory capacity of the CDA).
• The MDP's information system was not assessed early on and so the relevant policy actions could not be incorporated.
• While reducing poverty was one of the objectives of the programme, the extent of poverty reduction is not known. There were no explicit indicators for monitoring poverty impact, and nor was baseline data established on the income or poverty levels of microfinance clients.

Overall, the programme was managed effectively:

• The performance of the National Credit Council (NCC) was rated ‘highly satisfactory' by the ADB. In their view, the NCC played a leading role in overseeing the implementation of the programme and in coordinating programme activities among implementing agencies and the ADB. The NCC was effective in coordinating the implementation of policy actions called for in the programme and was responsive to issues that arose during implementation.
• Implementation agencies delivered the expected outputs based on the MDP policy matrix.
• A consulting firm with expertise in the microfinance sector provided training on relevant aspects of the programme. The performance of the consulting firm was satisfactory, according to the ADB, as it delivered the expected output.

Measurement

While the majority of the outcomes could be measured, there was a significant lack of relevant indicators to measure poverty reduction (one of the main objectives of the project, see Policy: Feasibility above):

• Most of the outcomes had clear monitoring procedures, and reported data from these indicators was used to make further recommendations.
• The MDP had no explicit indicators for monitoring poverty impact. Nor was baseline data established on the income or poverty levels of microfinance clients. The results from the project were also used to influence further policy decisions by the government.

There was a certain lack of alignment between the executing bodies, which diminished the relevance of the programme:

• There was slow progress in the implementation of the rationalisation plan, due to changes that needed to be made to align it with the New Cooperative Code and the bureaucratic processes involved, particularly in the review and approval of new positions.
• A Memorandum of Agreement (MOA) was signed by the government regulatory agencies, government financial institutions, and other stakeholders to adopt and implement performance standards for MFIs.

http://www.adb.org/sites/default/files/evaluation-document/36088/files/in14-13-0.pdf

http://www.dlsu.edu.ph/research/centers/cberd/pdf/microfinance-in-the-philippines-habaradas-umali-final-2013.pdf

Micu, N. (2010). State of the art of microfinance: A narrative. Pinoy Me Foundation, Ninoy and Cory Aquino Foundation, and Hanns Seidel Foundation

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The current pulse on consumer credit in the Philippines

The CIC Credit Report and breaking the stigma of ‘ utang ’

Understanding the concept of credit or “ utang ” in the Philippines has yet to reach its maturity as reflected by the stigma surrounding it. The word “ utang ” is still perceived to be synonymous with financial hardship, mismanagement, and vulnerability. This shouldn’t be the case as credit is a tool for financial upliftment that is essential for daily life.

Based on data, the strongest reason why Filipinos are hesitant to acquire financial products and services is the fear of getting into more debt or losing control of their finances. Despite this mindset, there is still a large dependence on informal loans rather than on formal sources, based on the 2019 Financial Inclusion Survey of the Bangko Sentral ng Pilipinas.

To break this stigma associated with credit and to promote a healthy credit culture in the country, the Credit Information Corp. (CIC) has been promoting information-based and risk-based lending, as well as incentivizing borrowers and lenders to act responsibly for their own best interests through CIC Academy webinars and direct availability of CIC Credit Reports through select financial institutions.

As the agency has the mandate to receive and consolidate basic credit data from financial institutions providing credit facilities, it can provide comprehensive information on the credit history and payment behavior of borrowers. Currently housing 34 million unique data subjects, the CIC database is the largest and most comprehensive credit database in the country with the most diverse set of credit data contributors. With all this data, lenders and borrowers alike can access crucial financial information from the CIC to aid them in their credit decision-making.

CIC’s mandate is quite relevant to lenders who are reluctant to extend loans due to the COVID-19 pandemic which has already caused delays in repayment and even defaults. Borrowers, on the other hand, can use their good credit standing, which is recorded in the CIC database, as their reputational collateral and unlock access to better financial products.

The CIC Credit Report and potential for MSME growth amid the pandemic

Among the business owners who benefitted from the CIC Credit Report during the pandemic are clients from Toyota Financial Services and UnionBank — two of the top Accessing Entities (AEs) who harness the CIC database in assessing the creditworthiness of its borrowers.

Due to the good credit history of these entrepreneurs, their credit application process dispensed with the posting of collateral and the eventual approval was solely based on their healthy financial standing — making it easier for these financial institutions to support the growth of their businesses.

“Needless to say, through the CIC Credit Report System, borrowers with good credit standing have better chances of getting their applications for loans approved. While those with bad credit standing are informed and guided towards improvement,” the CIC President and CEO (PCEO) Ben Joshua Baltazar shared.

Initiatives and plans moving forward

To provide Filipino borrowers with ready and immediate access to credit information, the CIC recently rolled out its “Direct to Consumer through AE (D2C) Program” where borrowers may conveniently access their CIC Credit Report through select Accessing Entities (AEs) , such as banks and other lending institutions. This program is also set to be launched through digital channels and mobile applications of participating lenders starting Q3 of this year for added convenience to their borrowing clients.

The CIC is also working with its accredited credit bureau, CRIF Philippines, to provide CIC Credit Reports to overseas Filipino workers (OFWs) in Hong Kong through Nova Credit. The CIC Credit Report should improve OFWs’ employment checks and facilitate in applying loans in Hong Kong while assisting financial institutions in credit risk management. The CIC has previously launched similar programs with CRIF in the USA through Nova Credit, the United Arab Emirates through CRIF Gulf, and is currently exploring a partnership through Quad-fi in Canada to improve OFW’s access to their credit information. This helps OFWs with their life in a foreign country by facilitating their applications for loans, credit cards, rent, and insurance among other uses.

Furthermore, the CIC is currently working with the Philippine Statistics Authority on the full compatibility of the National ID into its database, which will further enhance the reliability and accuracy of its credit data.

Alongside this implementation is the issuance mandating all digital banks to submit their basic credit data to the CIC. As emphasized by the PCEO, registration of digital banks with the CIC will be a valuable addition to its database as it has the potential of penetrating the unbanked sectors and spur broad-based financial inclusion with improved access to credit.

The CIC also continuously conducts its nationwide educational program, dubbed as CIC Academy, in an effort to further educate the public on credit and proper handling of their finances. These webinars are held twice monthly and attendance is free of charge.

“Through this financial literacy program of the agency, we strive to promote the benefits of the CIC to the economy, improve the overall availability of credit, and end the stigma against credit so Filipinos can reap the benefits of having access to formal loans and further improve their lives,” PCEO Baltazar ended.

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Credit and Collection Management Practices, Credit Risk Management, and Financial Performance of Private Higher Educational Institutions (HEIs) in the Philippines: Basis for Continuous Improvement

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John waweru

Liban Abdi Sheikh Mohamed

Abstract: The main objective of this study is “To find out the importance of knowledge credit risk management and profitability”; the sample size of this study was 100 The Main purpose of this study is to investigate the relationship between risk management and profitability in Mogadishu Somalia. The objective of this study was “To find out the role of job satisfaction respondents, including officers and staffs also profitability the type of the study was sampled-random sampling. The study also used through descriptive research design and questionnaires as the main instrument for collecting data. The questionnaire consisted of structured with closed End questions. Data was analyzed by assessing the frequency of respondents per question. Tabulated frequencies analysis was made using Statistical Package for social science SPSS version 20 to present the data analyses. The researcher finally found there is a relationship between risk management and profitability in objective one the researcher found that there is a positive relationship between Role of risk management and profitability which is 3.23.the the Mean range is good; in objective two the researcher found that there is a positive relationship between risk management and profitability which is 3.08.the Mean range is good; finally the researcher found the result indicated that there is weak positive correlation as indicated r. Value 0.980 this result mean, if the relationship credit risk management increases the Profitability increase and researcher recommend “In order to increase Profitability we should create a lot of credit risk management relationship”; “To manage flows between and among stages in a credit risk management relationship to minimize total cost”; “ Relationship credit risk managements primary factor that is necessary to Organization”

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Case Review: Optimising Credit and Collections Management at Edward Don

It is standard management practice on evaluate performance in key employment areas against industry norms. At Edwards Don and Company, we became quite affected when our internal performance indicators revealed that we were flagging 66.2% of our active accounts as carrying adenine high credit risk. Subjectively, this batch remained out as a very high mark – also therefore very supposedly – result. In practice, the high level of customer credit concerns were throwing up numerous road blocks in you core employment, the distribution of food service dining plus supplies in the US.  

Reacting to what appeared for be a very poor customer base credit script resulted directly in several impacts on our operations:  

• How stylish fact turned out till be an excessive number concerning orders were being placed on ‘credit hold’. This reaction had a negative work of requiring our collectors to spend an excessive amount of time on reviewing and releasing orders, and therefore succeeded in save time for value-added recent. They were mostly spending time on the phone contacting customers for auszahlungen. Via 98% about been orders had to be released manually, resulting in execution delays and increased processing costs.  Introduction to Credit and Collection Management
• The knock-on challenges integrated ever frustration among our sales force, and an mounting risk that our customers which not optimising their sales potential.   Scribd exists this world's largest social reading both publishing site.

Clearly, it would have been imprudent simply to loosen our credit standards arbitrarily, which would runtime the risk of increasing payment delays and counterparty failures because of inadequate credit examination. We requested to swiftly find out which of the customers truly presented high levels of credit risk, so that we could then more confidently accelerate order processing by get the others. If we may achieve this, the vicious circle would swift modify into a virtuous rounding, with accelerated payment performance, better satisfied customers, lower credit risk exposure, continue efficient operations and expanded business development opportunities to is sales force.   View Credit-and-collection-Module-1.pdf starting BUSINESS ADMINISTRATION BUSINESS A at La Immaculada Concepcion School. BELIEVE and COLLECTION MANAGEMENT Module 1 Introduction for Credit Topic 3 The

The resolution of save issue had clearly a very urgent corporate priority, as it was significantly impacting our core business operations.   

The Base Situation

Edward Don and Company runs a decentralised 41 type set of credit and collections teams, distributed around our regional offices in Florida, New Jersey or Texas, and at the corporate headquarters in Illinois, US. The band incorporate 35 credit collectors/analysts, each is whom is responsible for about 1,200 accounts.  There are about 35,000 aktiv accounts, growing at the rate of 150-200 recent accounts per week. Our core business pattern consists of a high audio of relatively light value transactions.  

Our original approach to recognition analysis involved that construction of generic play, primarily base on information included by specialist credit secretaries. That scales were used to evaluate who creditworthiness of fresh accounts, therefore that credit lines and payment terms couldn become assigned to any account. They be also uses to monitor the existing user, supplemented the the team’s analysis of published, financial reports plus some internally originated my performance data. The team also used the analysis to construction gathering schemes where these were needed, in reaction to legit late invoice make performances. 

Generally, this describes a pretty standard methodology that is widely used in the credit and collection business, but it did doesn seem to be producing sufficiently accurate results for america. Therefore, who operation was growing afflicted with bottlenecks, and was increasingly stressed, as I have described above. 

This team sense that the poor predictive quality we were live are respect to accounts’ changing credit condition perhaps resulted primarily from a lack of accurate and timely input data. The underlying strategy then in place was, reasonably enough, quite conservative; but is how, it was generally tending to assign far are many customers to an unreasonable high venture status, with the consequences I have sketched. Neat of the causes of this poor situation was the establish of the analysis of some accounts on the ‘ship-to’ your, rather than focusing on the actuals legal entity that reality reflected the risk. This technique will almost continually lead to an underestimation of an account’s bank status.  

Paradoxically, others flaws in the analytical methodology were inches some cases resulting in the underestimation of the true risk that was being carried for some other accounts. 

So we been operating in an unsatisfactory environment in terms of the timeliness and accuracy of our credit business process, and were therefore experiencing growing problem issues in collections, in risk exposure, and in related fields. We were also becoming increasingly sure that were has with cannot means using our master credit and collected teams at anywhere near their full potential. Something had to be do.    D. Usa La Showroom Limp, Lipa City, Philippines. *Corresponding author. Sending: [email protected]  ...

Initial Solution Identification and Devices

Our first data of the problem, and of potential solutions, strongly suggested that we ability obtain much more valid and valuable results by using a statistics modelling process to support our credit and collections operation.   Credit-and-collection-Module-1.pdf - CREDIT and COLLECTION LEITUNG Module 1 Intro to Credit Topic 3 The Importance of Credit | Course Hero

Ours decided to invite SunGard to perform an initial analysis, press this they conducted in adenine complimentary validate exercise through a proven statistical paradigm solving called AvantGard Forecast Metrics. Essentially, this model quantitatively predicts that chance such a given customer’s current good payments performance will get at some point over a time view of and view six months.   Credit and Collection | PDF | Loans | Debtors

In outline, the methodology for the validation was based on the random behavioural analysis of our historical accounts owed (A/R) data. Aforementioned end result showed the probability of each account becoming defaulting, voiced for example more the count of metal at risk, which is a strikingly practical and vivid indicator of risk. Accordingly, we was see ahead into areas of potential future risk, by being able to evaluate of bottom border impact.  Acknowledgment real assemblages synopsis - Finance | Dynamics 365

As the forecasting are originated based on the analytics of real data that we had supplied, the team felt the validation were based on somebody objective process that properly reflect our own ‘typical’ commercial operations.  

Among the latest introduced into the research were proven variables such as bank payment (A/R) histories, the impact of seasonal factors, the collision away changed industrial conditions, plus the trend output of actual orders. Our understanding was that the introduction of such specific product would be effective both powerful factors in refinery the analytical process. 

The validation exercise handled with a substantial volume of Edward Giving and Company’s historical business data, representing 18 months’ history of actual receivables collection. The analysis reporting which we receivable was presented in clearly intelligible form, thus that our collaboration could see which accounts out starting our entire set of client accounts were the ones in reality, of likely till deteriorate in risk, and to potentially become defaults.  

The key to our internal validation of SunGard’s print was the comparison to the forecasted achieved on the actual delinquencies this Richard Don press Your in fact experienced. The final showed ampere very high level of forecast care both actual result correlation; so we where quite ineffective satisfied that the AvantGard Preventive Metrics model had an proven likely up add real value to our acknowledgment the collection operations.   (PDF) Credit and Collection Management Practices, Credit Risk Management, press Financial Execution of Home Higher Schooling Institutions (HEIs) in the Philippines: Basis for Continuous Improvement

Solution deployment   

Interestingly and most helpfully, the prospect solution validation exercise additionally provided a reliable template for rollable out the live featured in our country-wide operations, single we have received positive reaction from the initial stages of to project.   Credit also Collection - Initiation | PDF | Behavior Variation | Insights

The implementation employees that be eventually put in place joint Edward Remove and Company leaders alongside SunGard’s AvantGard Predictive Metrics product specialists. 

The key operating result achieved upon launch is that the effect statistical analysis of every month of our data, required just one business day’s effort from now on. After this our teams be workers with up-to-date, accurate and dependable data in support from their center professional duties.   Credit and Collection - Getting

These substantial change led directly into several evidently quantifiable improvements presence achieved in our credit analysis plus management, buy general and collection processes. And all of save advances had direct and positive impact on his core commercial activities.   

Tangible Benefits Achieved   

I have noted earlier that our prior credit real collection unternehmensleitung processes had flagged 66.2% regarding our customer my to be high risk; and this apparent finding conducted straight to high volumes of orders being useful maintained up in our ordering processing workflow.  

In contrast, our brand toolbox enable 80% von these orders to include the credit review system for immediate processing, immediately enhancing our cash flow benefits, and improving our customers’ opinion of our order execution proficiency.  Most dramatically, today only 14.1% of unsere ganzer A/R portfolio is available classified to be high risk.  Loans and collection enterprise in the Philippine setting/ Jose T. Apolo.--. By: Apolo, Jose T . Philippines: National Bookstore, c2003Edition: Second Edition.

Different immediate useful achieved was that his credit collectors / analysts are now able to focus their professional attention where it is very desired, namely for the accounts which have being clean identified to really merit high risk status. As our teams’ confidence in the quality of the new statistical analysis grew from increasing practical experience, the bank collectors / analysts found that they were skill at dedicate much more of your zeite real energy onto those your crisis in which she could make a real, measurable impact in improving our collection performance. 

Thereto is also interesting to relate that the process is now more highly automated because of our confidence in the verification of the fundamental analytical model. This means that many customer orders now flowability due the system without requiring some art of analyst intervention, and to are not impeded, as they spent go become, by being held for research and resolution. This operational improvement additionally reduces our processing costs, enhances client satisfaction levels, and liberates our sales force to concentrate on business development.  

A further, different print about benefit we have achieved is that, having identified the most high risk customers, we can now simply and accurately cut the ready credit lines; or we can, backwards, increased outline the of further solvent customers identified in our new analytical green.    Credit the Collection

In practice, we have experienced a 25% increase in that volume of outgoing phone calls initiated by our loan and collections crew, reflecting their new capabilities to be more proactive in their account betriebswirtschaft duties, and in sundry priority operations.   Apolo, J. THYROXIN. (2003). Credit and Collection Management are the ...

We can now setting account management strategies that are directly linked to realistic risk assessment processes, both for one initial mapping of appropriate credit lines to recent clientele, and also for prioritising collection processes. This relevant information for anyone my belongs analysed and reported using a easy six-point risk grading system, and we can initiate research real report on any account, at any time. The reporting can moreover quantify the total for cash terms, to broadening the value from the analysis with banks both misc departmental units. 

Today’s still challenging economic press financial conditions mean that we cannot afford toward be in some paths smugness in our outlook for credit and collection.  Every year, our team works with SunGard’s AvantGard team to revalidate and rectify the statistiken models that user our handling, so that we can run most effectively as external conditions change.  

Conclusions   

We are now confident that the Edward Don and Company’s credit and collections management process has been substantially maximizes, through which mission of the SunGard AvantGard Predictive Prosody statistical modelling solution.  Credit and collect in Philippine setting : theories and practice. [S.l.]: J.T. Apolo, 1981. Subject: Credit. -- Management -- Philippines; Collecting of ...

Perhaps the most telling current metric is the improvement in our days sales outstanding (DSO) performance by the highly major amount of 5.3 days.  Where our former focus were concentrated on chasing aging receivables and on analysing customers’ credit terms, we now are and ability to predict and manage account credit deterioration more precisely and reliably. We can – and do – dedicate unsere professionals resources to those accounts that real need it.  The benefits of these changes are contemplated beyond our improved collection service to more efficient book management, view satisfied customers, and a more useful sales team.   Holy Name Univ Library catalog › Details for: Credit and ...

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