Monitoring and Early Warning System – Identify Trends before they become Problems

This is the fifth article in a series of blog posts in which we introduce the concepts of knowledge-based lending for SME business. In this article, we introduce the monitoring process and the early Warning System. Those process steps are essential for the successful execution of SME lending. While each financial institution needs to conduct a thorough risk assessment and approval, the actual monitoring of the exposure it is equally if not more important. Clients will experience challenging situations over the life of a transaction and are in need for support from their financial institution.

We have integrated a detailed monitoring process into the development of Q-Lana, the digitization platform for knowledge-based lending. This allows the early discovery of problems developing with lending exposures, far before an actual payment default. We have developed Q-Lana as a comprehensive, fully customizable and intuitive, filled with smart tools and analytics to support financial institutions. For more information about Q-Lana, please read below and contact us

Q-Lana enables financial institutions to develop sound credit-monitoring and risk-mitigation capabilities. The earlier a financial institution identifies and responds to deterioration of credit risk, the better is the possibility to avoid problems, including defaults. Q-Lana identifies risky customers and exposures far before they experience payment problems and can easily differentiate between inability and unwillingness to service a loan. Critical customers can be placed on an internal watch list, to allow a more thorough monitoring of the relationship and the early development of mitigating solutions. This also helps financial institutions to support its clients during critical phases where they are in urgent need for such support.

 

Monitoring Process

A financial institution shall focus on the monitoring of the borrowers right after the disbursement. This relates to the process of regular monitoring of performing loans. The key steps of the regular monitoring are summarized in the following picture

 

There is high importance for the monitoring, especially of long-term exposures. It is quite common that clients will experience financial or operational difficulties over the course of a longer credit exposure. Q-Lana has developed a structured process for the monitoring of exposures after disbursement. The specific process can be adjusted for the requirements of a financial institution. An example of the necessary steps is provided in the following table:

Initial Supervision Report: A financial institution requires the loan officer to conduct a follow-up visit within a certain number of days after the disbursement of the loan. This is primarily serves the purpose to verify the actual use of funds and to assess the project performance with the newly injected funds. The loan officer is required to comment on any observation be it positive or negative and to verify whether the conditions that were used to justify the approval are still valid. The form provided through Q-Lana has space for suggested follow-up items. Q-Lana will define internal tasks for each of those follow-up items to ensure that they are timely addressed. The exposure can be moved to watch-list.

Monitoring – Ongoing Monitoring: subsequent to the initial visit a regular monitoring process is started the by the loan officer. The rules of the specific institution define the frequency of such monitoring visits. For borrowers of weaker credit quality, such meetings shall happen on a more frequent basis. In each of these visits, the client’s performance is assessed and compared to the information given here in the initial loan application. Any comments or observations are noted in a report. Specific follow-up items can be set and tracked. In addition, the loan officer needs to suggest the time for the next follow-up visits and comments on the need to move the borrower to the internal watch-list.

Monitoring – Other Observation: in addition to the regular reporting’s, there is the possibility to submit “Other Observations”. This can include informal visits, observations or comments made “on the street”, reference to products, news or any other relevant information.

Early Warning System: With an effective Early-Warning System (EWS), credit losses as well as capital requirements can be reduced through de-risking. This will improve the institution’s capacity to take risk, increase returns and improve the capital productivity. For the client, the benefits include lower pricing as well as the active support from a qualified expert in the management of critical situations. The EWS is well integrated into the monitoring process of Q-Lana

Q-Lana has integrated the EWS into the standard procedures and allows the institution to identify borrowers at risk through a structured monitoring process and the integration of warning signals. The Q-Lana EWS is built on several categories of indicators, allowing the identification of critical exposures based on electronic information/automated triggers, expert knowledge about the exposure, as well as external information. The categories are:

  • Quantitative Criteria – the EWS analyzes the utilization of credit lines by borrowers, borrowing activities, cumulated arrears days and other indicators. Early warning indicators are triggered if certain patterns are discovered. For example, as soon as the utilization reaches certain limits and remains at that level, the exposure is flagged as riskier.
  • Covenant Monitoring – the loan documentation might include affirmative and negative covenants, representations and warranties and other requirements. The monitoring of the compliance with these covenants is part of the EWS. Here, not only the breach of certain covenants is monitored but also the absolute value of quantifiable covenants. In this way, trends can be assessed allowing to identify weakening credit quality at early stages.
  • Financial Analysis – on a regular basis, the institution receives financial information from clients. This financial information is tracked within Q-Lana and assessed through the review of certain ratios. At the same time, the institution can update the internal rating system with the new financial data, allowing the calculation of new rating values and incorporating the trends of the rating development into the EWS
  • Early Warning Questions – Q-Lana has developed an early warning questionnaire which is part of the monitoring package. Loan officers can apply this questionnaire during the regular monitoring visits or when a visit is requested through the placement on watch-list. Details about the assessment questionnaire can be found in the attachment.
  • Early Warning Monitor – all observations of the EWS are summarized in a dash dashboard which visualizes the specific monitoring results categorized along the line of the above-mentioned criteria

Internal Watch List – Special Monitoring:  if an exposure is identified through the monitoring process as deteriorating or critical, it is placed on the internal watch-list. The number of days this exposure is placed on watch-list is tracked. It is required to prepare a special assessment of the exposure considering the reasons for placing it on watch list. Based on this initial assessment, the next steps are defined, which can include (in order of severity):

  • Removal of the client from the list as the identified issues have been deemed mitigated
  • Keeping the client on watch list for a more frequent monitoring procedure
  • Developing measures for risk mitigation in order to improve the risk profile of the exposure and/or support the client in the recovery from a current problem
  • Moving the clients to special servicing/recovery, in case the client is in default and issues have been too severe for a return to normal conditions in the future

Loan Collection Process – Defaulted Loans Once a client is in default, the financial institution applies a strict procedure to collect the funds or to start additional measures quickly in case the collection seems difficult or impossible. The specific steps for the collection process depend on the financial institution. A sample process is described in the following table

Post Mortem Analysis – by closely analyzing defaulted loans and discovering the patterns behind defaults or the reasons, a financial institution can gain valuable information to improve the risk assessment of individual loans in the future. Reasons for defaults can be “avoidable mistakes” such as improper credit analysis and reliance on non-verified information, or “unavoidable reasons” such as sudden health issues for the borrower or natural disasters. [60] days after the default, the loan officer is required to fill the Post Mortem Questionnaire.

The Post Mortem Questionnaire are available to those loan officers who have loans with payments in arrears above 60 days. The loan officers have 5 business days to fill and return the questionnaires. The information is analyzed, aggregated and provided to the team as a training measure.

We believe in the high importance of a structured monitoring process, specifically in SME lending. While the initial analysis is important, the monitoring is even more relevant for the performance of the loan portfolio. Q-Lana takes this into consideration through the list of tools and instruments provided to the financial institution. Here even more the importance of knowledge-based credit risk management becomes obvious. The borrowing clients will appreciate the sophisticated approach to monitoring as this clearly positions the financial institution as partner to the business

About the author:

Christian Ruehmer is the Co-Founder and CEO of Q-Lana. Christian has over 25 years of experience in finance working for several large international banks in the areas or Risk Management, Credit Portfolio Management, and Investment Management. Christian has been an advisor in this sector, working with over 75 MFIs, banks, and international organizations, primarily in developing countries. He is the head of Risk and Compliance at Bamboo Capital Partners and sits on the board of several companies in the finance sector

About Q-Lana:

Q-Lana provides a digital platform to transform traditional lending into a knowledge-based, risk focused process. By digitizing the initial assessment, application and approval, monitoring and portfolio management process, as well as the collection, the financial institution generates significant knowledge about the performance and the credit quality of the target clients. This can be used to improve lending decisions, avoid losses and provide advanced support to the clients. Q-Lana replicates the existing lending process and allows for modifications to apply best practice. In addition, Q-Lana provides intelligent reporting and customized risk advisory. Q-Lana is based on a SaaS model that integrates well with existing core banking software and can be implemented within a 3-4 month timeframe. 

Contact:

christian@q-lana.com

Loan Application Process – Transparency and Collaboration

This is the fourth article in a series of blog posts in which we introduce the concepts of knowledge-based lending for SME business. In this article, we take a fresh look at the overall lending process in a financial institution focusing on SME loans. In each of the steps we show some ideas how to make the process more efficient and more substantial. We show how to improve the steps through the use of knowledge and contextual intelligence.

We have integrated all the ideas into the development of Q-Lana, the digitization platform for knowledge-based lending. Our clients apply those tools at their discretion, immediately or in future process improvements. We have developed Q-Lana as a comprehensive, fully customizable and intuitive, filled with smart tools and analytics to support financial institutions. For more information about Q-Lana, please read below and contact us

Simplicity and Speed vs Complexity and Completeness of Information

 

The knowledge about the SME business in the target markets is the greatest competitive advantage of SME focused financial institutions. The challenge is that this knowledge is mainly collected in the heads of the relationship managers and loan officers who deal with the clients for many years. Making this knowledge available within the institution can dramatically improve the quality of the risk assessment and provide superior service to clients.

The customer experience in financial institutions often lacks simplicity and speed. In this way, financial institutions give away their competitive advantages of maintaining a good relationship to other providers, specifically to those with ties to fintech. Offering streamlined application processes, transparent decisions, and simple terms and conditions for the products targeting SMEs is a way how a financial institution differentiates itself from its competitors.

Clients prefer simple and understandable products with transparent pricing. Most products can be classified into three basic forms of borrowing:

  • Term loans in which a lump sum is received and repaid under certain terms and conditions
  • Lines of credit which allow the borrowers to utilize and pay only for the amount that is needed
  • Guarantee and insurance products in which no cash is exchanged initially, but the financial institution provides for specific contractually defined situations

Simplicity and speed in the decision process are often seen in the opposite of diligence and precision. The latter are key criteria for successful SME lending. When developing Q-Lana we did not compromise on quality and diligence of the assessment. Rather we used technology to simplify the collection and utilization of knowledge which will help the financial institution offering the precise products and services a specific client requires at a given point in time.

In the following, we pick out specific components of the lending process, where Q-Lana provides enhanced methods for the collection and assessment of client information. They are grouped in line with the process flow.

Company Analysis

To conduct the analysis of a company, Q-Lana provides several tools for the assessment of company, such as SWOT Analysis, Business Canvas, or Business Planning. The second part of this blog series provided the background to this. Q-Lana lifts these components even further as it provides the users the ability to search for assessments of comparable companies by sectors, regions or other criteria using contextual intelligence. Through such a peer assessment, the understanding for the specific situation of the client deepens and the loan officer can gain a better understanding of the companies in a certain sector or region.

The Q-Lana user has always access to the full resources analytics, training materials, instructions and background information. This information is provided through the research done by the financial institution as well as by Q-Lana’s Risk Advisory Services.

Risk Assessment

The analysis of the borrower’s financial information takes a key part in the assessment of credit risk in a loan request. The target clients of the financial institutions in developing countries might not have audited financial statements or no structured financial statements at all. In the development of Q-Lana, we have considered this potential challenge. Q-Lana supports analyst in the preparation of financial statements. Q-Lana uses a customizable Excel template to collect and analyze the financial statement in the required granularity. Calculations can be added, and ratios calculated on separate worksheets of the Excel file. The first worksheet of the template captures the summary. The loan officer can upload the template to Q-Lana, where the specific values are captured and stored. Once the upload is completed, the financial statement can be analyzed against comparable clients through the contextual intelligence.

Q-Lana provides the ability to collaborate across teams in the assessment of a client and in the loan request. Questions can be raised across the network and comments provided on specific sections or the complete application.

A structured risk assessment is at the core of a loan request. Q-Lana provides a template for the risk assessment. The loan officer identifies the risks and categorizes them. The risks and mitigating factors are described, leading to a summarizing risk assessment. Over time, the financial institution builds a library of identified risks and can benchmark new transactions against the risk assessment of approved or declined loans.

As loan request moves through the approval process, the respective approval authorities can comment on the analysis, request further assessments, include specific approval conditions or forward the transaction after approval to the loan administration.

 

Approval

The fully flexible workflow functionality of Q-Lana allows the financial institution to replicate any type of approval procedures. Whether a department or a committee needs to approve the transaction or simply sign off on the request can be customized within the platform. The financial institution can define separate processes by product if the workflow differs.

As a loan request is forwarded for approval, the approval authority can compare the key components of the approval to other approved/decline transactions of the past. This will help with consistency of decisions and approval condition

Any decision is tracked within the Q-Lana platform, including the related minutes. In those minutes, conditions for the approval can be tracked.

Pre-Disbursement Checks

Once a request is approved, it is forwarded to the department preparing the disbursement. Here, the documentation is completed, collateral perfected and relevant copies are stored in Q-Lana’s document management functionality. Q-Lana maintains checklists, individualized for the institution, about the specific steps required before a loan can be disbursed. The completeness of the loan documentation can be monitored.

Q- Lana maintains a separate database for the management of collateral. Each collateral is tracked in the database and can be linked to to a specific transaction in the pre-disbursement process. The separation of the collateral management allows to track collateral in a more efficient way. Q-Lana allows to set reminders to update collateral documentation such as insurance certificates

Once a transaction is completed and disbursed, a dedicated monitoring and collection process follows, supported by Q-Lana. We have developed a monitoring process which keeps track of performing and non-performing loans. Through a combination of analysis of covenants, utilization, payment discipline and an early warning indicator checklist, loan exposures are tracked. Potential problems are identified and addressed before an actual payment default.

A detailed description of the monitoring process will be provided in the next blog article. Please contact us for comments and questions.

About the author:

Christian Ruehmer is the Co-Founder and CEO of Q-Lana. Christian has over 25 years of experience in finance working for several large international banks in the areas or Risk Management, Credit Portfolio Management, and Investment Management. Christian has been an advisor in this sector, working with over 75 MFIs, banks, and international organizations, primarily in developing countries. He is the head of Risk and Compliance at Bamboo Capital Partners and sits on the board of several companies in the finance sector

About Q-Lana:

Q-Lana provides a digital platform to transform traditional lending into a knowledge-based, risk focused process. By digitizing the initial assessment, application and approval, monitoring and portfolio management process, as well as the collection, the financial institution generates significant knowledge about the performance and the credit quality of the target clients. This can be used to improve lending decisions, avoid losses and provide advanced support to the clients. Q-Lana replicates the existing lending process and allows for modifications to apply best practice. In addition, Q-Lana provides intelligent reporting and customized risk advisory. Q-Lana is based on a SaaS model that integrates well with existing core banking software and can be implemented within a 3-4 month timeframe. 

Contact:

christian@q-lana.com

 

 

 

Rating and Scoring – Ask the right Question at the right Time

This is the third article in a series of blog posts in which we introduce the concepts of knowledge-based lending for SME business. In this article, we look at the opportunities and challenges provided through rating and scoring methodologies. We provide a general overview of those tools to support lending and monitoring. We show the limitations with regards to automated decision support.

Rating and scoring methodologies are integrated into Q-Lana, the digitization platform for knowledge-based lending. We have developed Q-Lana as a comprehensive, fully customizable and intuitive, filled with smart tools and analytics to support financial institutions. For more information about Q-Lana, please read below and contact us.

The availability of technology has created a wave of initiatives to simplify the lending process through scoring and rating methodologies[*]. The expectations related to rating and scoring methodologies are high, if not exaggerated. In this article, we explain the various methodologies in simple terms, without getting too mathematical. We show how they can support financial institutions in an intelligent way in the decision making and monitoring of loan exposures to corporates and SMEs.

Rating and Scoring in simple Terms

Rating and scoring models aggregate information about credit risk into a single number, a score. This score shall classify the exposure, ideally by associating a default probability. Combined with the estimated loss in the case of default and other input factors, the total expected loss of a loan exposure can be calculated. Regulators appreciate the efforts made by financial institutions to analyze the risk in a structured way. This allows a better alignment of the regulatory capital requirements with the actual economic capital needs based on the risk profile.

Three types of information are used for the assessment of a rating:

  • Quantitative data about the borrower, objectively measurable numerical values, derived from primarily financial information
  • Qualitative data, derived from assessment and monitoring of the borrower, the business strategy, management qualification and other relevant factors
  • External information about prices and economic indicators which are deemed relevant for the performance of the borrowers

Types of Rating Models

The rating and scoring models, used by financial institutions can be split into three main categories:

  • Heuristic models – previous experience and subjective observations, combined with business theories are driving the calculation in those models. There is usually no statistical validation or optimization. The heuristic models are frequently in the form of rating questionnaires which are built by credit experts. Ideally those questionnaires have clearly defined answers, leaving little discretion to the users. The quality of the models depends on the experience of the developers and users as there will always be judgement involved in the answers. The actual grading associated with the selected answers is subject to the presumed impact on the credit quality. Qualitative assessments are used, in which specific criteria are assessed and classified for example as “good”, “average”, or “poor”, based on comparative analysis and judgement. The usefulness of heuristic rating tools depends largely on the detail of instructions and the discipline of the users in assessing borrowers. The result of a heuristic rating is a score, which can be categorized into a rating classes (e.g. AAA, AA, A, ..). The number of rating categories can be freely assigned. Regulators have certain minimum requirements to accept the rating as an approved internal rating model. The heuristic models are the basis of the borrower/obligor ratings of most of the established rating agencies. Through back testing the rating models over longer periods of time, rating agencies fill migration tables which allow to derive the default frequencies for rating classes. Yet, established rating agencies tend to shy away from making a strong connection between those factors in the actual rating process.
  • Statistical models – the statistical models attempt to verify a hypothesis with regards to the predictability of actual defaults by using statistical techniques on historic data. The quality of the statistical tool depends very much on the empirical data used in the model development. This requires a high quality data set about a large number of borrowers. There also needs to be a sufficiently large number of actual defaulted loans, to calibrate the model. Data quality is another key criterion to ensure that the statistical model produces meaningful results. The statistical instruments used to calibrate a statistical model range from regression analysis, discriminant analysis, profit analysis, and logistic regression to more advanced methods, such as linear programming, decision trees, and neural networks. The criteria used in statistical models include for example demographic data (age, gender, nationality), data derived from past lending activities (credit bureau information, past arrears data) as well as more recently also psychometric data such as the time it takes to answer certain questions.
  • Causal Models are the third category of rating models. They are based on financial theories, such as option pricing models. It is assumed that a credit default will occur when the economic value of the borrower’s assets falls below the economic value of its debt. Pre-condition for such models is the availability of market prices for debt and equity, which makes the model difficult to pursue for most borrowers.

In real world applications, the methodologies are often combined. Most common are rating models which use a combination of  heuristic and statistical data.

Issues with Rating and Scoring

In our work with financial institutions to develop and implement rating and scoring models, we often came across the same issues related to the  availability and quality of data:

  • The financial institutions have not conducted a sufficiently large number of loan approvals to properly calibrate a statistical scoring model. While there is no minimum number of data sets, it is recommended to have at least 5000 or more homogeneous loans to validate a statistical model. The idea to use data from other institutions or potentially from purchased databases is usually not compensating to the lack of own information
  • There also needs to be a sufficiently large number of defaults (>5%) for the right calibration. In practical applications, we often have to redefine the default criteria, in order to come up with a sufficiently large number of “bad cases”.
  • Data quality is challenging in most of the financial institutions. Demographic data is usually overwritten by the accounting system: when downloading the information of a borrower who took 10 loans from one financial institution over the last decade, the demographic data associated with this loan reflects only the latest information but not the information from previous loans. For example, the borrower’s marital status might currently be “widowed”. When downloading the information of the past loans from the accounting system, the marital status is the same for all the loans, as it is derived from the database of key client data. The borrower might have been “single” for the first loans, “married” for several later loans and has only been a “widow” in the last loan cycle.
  • Financial institutions use open text fields for the collection of data, instead of drop downs. As a result, the number of different values for certain categories is exaggerated. When information is collected in free text format, similar values are expressed differently. For example, it might be a difference whether the client is based in the “Western region”, “West region”, or simply “West”. There is a difference in descriptions, whether the client is classified as “Head of Marketing”, or the “Marketing Head”. Those quality issues are on top of the other expected problems, such as available data, wrong data, or data classified as “other”.
  • Financial institutions with a “bricks and mortar” business model have a largely implicit natural pre-selection of borrowers. A loan officer will not prepare an application for every potential borrower and will only present cases to the credit committee which have a chance of approval. Such “pre-declined” low quality borrowers will not be captured in the systems. In addition, traditional accounting systems do not capture transactions which are declined or which are not completed by the borrower.

Those issues with regards to data quality and data availability make it difficult for financial institutions to develop statistical scoring models.

When developing Q-Lana, we looked at the most pragmatic ways of applying rating and scoring methodologies. Q-Lana as an integrated rating widget, which can be deployed in several instances in the lending process. The widget is set up to function as a heuristic rating tool, with the potential to include statistical rating components. We suggest using the rating tool for the following purposes in a best practice lending model:

  1. Assessment of the client’s business potential. Over the recent years, studies have been conducted to assess which companies have the best preconditions to become attractive clients for full-service financial institutions in the SME sector. For example, a study conducted by Center for Financial Inclusion lists the following criteria as important for small enterprises to grow successfully (Emerging SMEs – Secrets to growth from micro to small enterprise, October 2016, Christy Stickney): age of the borrower (ideally between 30 and 49 years old), exclusive dedication to operating a single business, business development based on experience and vision, maintenance of written financial accounts and several other criteria. The rating widget of Q-Lana can be used in the initial client assessment process, or to derive the business potential of a company. This can help the financial institution to offer specific targeted financial services to this company.
  2. Assessment of the credit quality for the loan approval. This is the most common application of the rating model. The Q-Lana rating widget allows the financial institution to define heuristic and statistical rating criteria flexibly for several borrower types in a variety of sectors. The rating widget is fully customizable with regards to the specific criteria, the quantification of those criteria and the weighting of the criteria against each other. A rating which is calculated in the context of a loan application can be used for decision support as well as for pricing and approval limits. From our perspective, more important than the absolute rating value is the comparison of borrowers against each other.
  3. Early Warning Rating. Q-Lana suggests adjusting the rating for the monitoring of existing transactions. As will be explained in a later chapter of this blog, high importance is given to the monitoring of existing exposures. A specific early warning rating has been prepared for the users of Q-Lana. This rating considers performance information of a borrower under an existing loan exposure and monitors trends in the development of credit quality. Such infoArmation is usually not available for clients during the application process. More important than the absolute value of the rating is the trend of the rating development of an individual borrower.

Q-Lana provides the rating tools in a flexible and fully customizable way. When customizing Q-Lana for a financial institution, we provide guidance on the customization of the tools based on our experience and best practice. The tools can all be updated over time as more information is collected and better assessments can be made.

We see this usage of the rating tools as the best way to take advantage of the technology, without overstretching the concept. In the next article of the blog, we will describe the process of loan application in which we added additional innovative tools and instruments for a better risk assessment.

About the author:

Christian Ruehmer is the Co-Founder and CEO of Q-Lana. Christian has over 25 years of experience in finance working for several large international banks in the areas or Risk Management, Credit Portfolio Management, and Investment Management. Christian has been an advisor in this sector, working with over 75 MFIs, banks, and international organizations, primarily in developing countries. He is the head of Risk and Compliance at Bamboo Capital Partners and sits on the board of several companies in the finance sector

About Q-Lana:

Q-Lana provides a digital platform to transform traditional lending into a knowledge-based, risk focused process. By digitizing the initial assessment, application and approval, monitoring and portfolio management process, as well as the collection, the financial institution generates significant knowledge about the performance and the credit quality of the target clients. This can be used to improve lending decisions, avoid losses and provide advanced support to the clients. Q-Lana replicates the existing lending process and allows for modifications to apply best practice. In addition, Q-Lana provides intelligent reporting and customized risk advisory. Q-Lana is based on a SaaS model that integrates well with existing core banking software and can be implemented within a 3-4 month timeframe. 

Contact:

christian@q-lana.com

[*] We use the expression “rating model” in the context of credit assessment of a borrower and “scoring” as a component of a rating model. In the industry, there is no clean separation in the use of the expressions

Business Assessment – Using Tools to understand the Client’s Business

This is the second article in a series of blog posts in which we introduce the concepts of knowledge-based lending for SME business. In this article, we focus on the steps to assess an SME’s business model, strengths and weaknesses as well as the business potential it might provide to a financial institution. The tools presented here are all part of Q-Lana, the digitization platform for knowledge-based lending. We have developed Q-Lana as a comprehensive, fully customizable and intuitive, filled with smart tools and analytics to support financial institutions. For more information about Q-Lana, please read below and contact us

 

In our first blog entry, we have claimed that providing financial services to small- and medium sized enterprises (SMEs) is among the most interesting and most challenging business areas for traditional financial institutions. Financial services for SME clients require specific understanding of the way those companies conduct business, the entrepreneur’s personality, strengths and weaknesses.

SMEs need Financial Institutions as Business Partners

Independent from the macroeconomic environment, SMEs always feel the challenge of economic developments. SMEs desire banking relationships which help them manage and grow their businesses during times of stability and times of turbulence.  SMEs rank cash flow management, credit facilities, financial planning and budgeting tools, cash management services and business growth advice as top financial needs. Forward thinking financial planning advice is critical to SMEs. A smart financial institution provides those services and converts the banking relationship into a business partnership, providing more than just punctual lending or transactional services.

Such a business partnership is best built on structured information collection and the ability to provide value added advise to the client based on expertise and experience. Traditionally, financial institutions have engaged in the assessment of the risk profile of a client only at the time of a credit request. This was both tedious and reactive. It was difficult to monitor a client’s development as the information was split across sequential loan applications.

Splitting up Information in main categories

Best practice breaks with this this sequential information collection process in two ways: it splits the collected information in four main categories and it allows to build knowledge independently from an actual loan request. The four main categories are:

  • Individuals, such as borrowers, guarantors, employees of borrowers, contractors etc.
  • Companies, borrowers and guarantors, independent from whether they operate as legal entities or as unincorporated ventures
  • Facilities, the actual loan products, based on contractual agreements and subject to terms and conditions
  • Collateral, which can have various forms, including real estate, other tangible assets, cash, guarantees, etc.

Often, loan officers collect information about borrowers from scratch, every time a new transaction is proposed, the digital platform simplifies this process by making past information available and by providing contextual intelligence about sector, portfolio and comparable companies.

Information about companies can be maintained and assessed, independently from the actual exposure. This also allows a simplified workflow during the application phase and improves the data quality through consistency.

Understanding a Client’s Business Activities

Successful Individual/SME lending is based on long-term client relationships. In order to build a trustful relationship and to be able to provide value added advise beyond the lending activity, it is important to develop a thorough understanding of the client’s business activity, the short and long-term goals. Loan officers can develop a knowledge base about the economic development of the target clients. By creating a professional relationship with borrowers, the loan officer is also cultivating the client’s improved willingness to pay.

Q-Lana provides several tools which can be customized for the specific purposes of the financial institution. Those include

  • SWOT Analysis. The analysis identifies a client’ strengths and weaknesses. The strengths relate to core competencies, whereas the weaknesses could lead to poor performance. It also examines opportunities and threats in the wider environment, coming from market, industry and more general trends. A SWOT analysis does not give specific answers. It organizes information as a basis for developing business strategy & operational plans. The simplicity of preparing a SWOT analysis can be beneficial as well as challenging. The following steps should be followed to prepare a useful SWOT:
    • List any issue you can think of that might affect the business. Those issues can be subjective as well as objective.
    • Sort the issues based on the SWOT categories
    • Sort each category first by relative importance and then by likelihood
    • Use a reduction process to limit each list to no more than five factors. For that purpose, remove duplicates and prioritize the items
    • Collect feedback from senior staff and others
  • Abbreviated Business Plan. Every company needs a plan about where and how the business is developing. A business plan can summarize  this information. Depending on the potential of a client and the importance of the relationship with the financial institution, the loan officer can support the development of an abbreviated business plan. The purpose of the plan is to set out the strategy and action for the business over the next 1 to 3 years. It is aimed to inform outside parties, such as potential lenders, investors, and staff about the plans of the company. The content of this plan needs to be short, as the details can be left to a more structured and complete plan. It is important that the plan is based on reality. The main sections of an abbreviated business plan are:
    • Business and Products: explain the currently offered products and services, as well as the history of the business. Cover topics such as current ownership, description of the products, factors that make the products and services different and beneficial for the clients. Cover the planned new product developments.
    • Market and Competition: define the target markets, including information about the size of the market, market share, market trends and potential drivers affecting the market segment. Explain the competitive situation, including the competing products and services as well as their advantages and disadvantages.
    • Marketing and Sales: this section answers the questions about the positioning of the products in the market, the pricing policy, promotion strategy, sales channels, and general selling strategy.
    • Marketing and Personnel: here, the structure and key skills of management and staff are identified. If necessary, the related recruitment and training plans need to be confirmed. The overall workforce needs to be analyzed including information about qualification, motivation, retention rate, productivity, and salary levels.
    • Operations: the capacity and efficiency of the operations and the planned improvements need to be described. What kind of offices/production space is available? How is the production organized? What technology and information systems are in place? What is the competitive advantage of the production?
    • Financial Performance: describe the historical financial information for the last years, with a focus on sales per products, gross margins, and overhead expenses. Calculate the key ratios with regards to the components of working capital. List the major capital expenditures for the next periods. Provide a forecast for the next years, including a clear explanation about the assumptions. If possible, carry out a sensitivity analysis with regards to the sales, and the profit margins.
  • Business Canvas is a strategic management and lean startup template for developing new or documenting existing business models. It has been developed by Alexander Osterwalder and Yves Pigneur[*]. The Business Model Canvas is a visual chart with elements describing a company’s value proposition, infrastructure, customers, and finances. It assists companies in aligning their activities by illustrating potential trade-offs. The nine building blocks of the canvas are listed in the following, including some key questions to develop the content of those blocks:
    • Customer Segments – Which customers and users are you serving? Which jobs do they really want to get done?
    • Value Propositions – What are you offering them? What is that getting done for them? Do they care?
    • Channels – How does each customer segment want to be reached? Through which interaction points?
    • Customer Relationships – What relationships are you establishing with each segment? Personal? Automated?
    • Revenue Streams – What are customers willing to pay for? How? Transactional or recurring revenues?
    • Key Resources – Which resources underpin your business model? Which assets are essential?
    • Key Activities – Which activities do you need to perform well in your business model? What is crucial?
    • Key Partnerships – Which partners and suppliers leverage your model? Who do you need to rely on?
    • Cost Structure – What is the resulting cost structure? Which key elements drive your cost?
  • Sector Checklists. Aside from understanding the company and its activities, it is also useful to assess the company within the sector it is operating. Q-Lana provides sector assessment checklists which can be fully customized at the initial use and updated over time. This helps in assessing the positioning of a company within a specific sector.

Using the tools as described above as well as number of additional instruments, such as Porter’s five forces model or five factors of production will help the financial institution to achieve the goal of better understanding the client and to build a business relationship with him. This process is independent from the actual lending activity and serves as a basis for the development of future business. In the next articles of the blog we will go deeper into the assessment of the clients credit risk, through the use of additional innovative tools and concepts.

 

About the author:

Christian Ruehmer is the Co-Founder and CEO of Q-Lana. Christian has over 25 years of experience in finance working for several large international banks in the areas or Risk Management, Credit Portfolio Management, and Investment Management. Christian has been an advisor in this sector, working with over 75 MFIs, banks, and international organizations, primarily in developing countries. He is the head of Risk and Compliance at Bamboo Capital Partners and sits on the board of several companies in the finance sector.

About Q-Lana:

Q-Lana provides a digital platform to transform traditional lending into a knowledge-based, risk focused process. By digitizing the initial assessment, application and approval, monitoring and portfolio management process, as well as the collection, the financial institution generates significant knowledge about the performance and the credit quality of the target clients. This can be used to improve lending decisions, avoid losses and provide advanced support to the clients. Q-Lana replicates the existing lending process and allows for modifications to apply best practice. In addition, Q-Lana provides intelligent reporting and customized risk advisory. Q-Lana is based on a SaaS model that integrates well with existing core banking software and can be implemented within a 3-4 month timeframe.

Contact:

christian@q-lana.com

*http://nonlinearthinking.typepad.com/nonlinear_thinking/2008/07/the-business-model-canvas.html