Financial analysis of yes bank by Saurabh Kumar +91 9990415104Saurabh Kumar
- YES Bank is a private bank founded in 2004 that has expanded rapidly, with the number of offices increasing from 118 to 428 and number of employees increasing from 2671 to 7024 from 2008-2013.
- Financially, deposits increased 76% while investments and interest income increased around 84% and 76% respectively over the five-year period. Return on equity rose 27% showing increased profits.
- Compared to other private and commercial banks, YES Bank has increased the number of offices and business per employee at a higher rate but has a lower increase in number of employees, possibly to control costs.
This document discusses MongoDB and common use cases for NoSQL databases and MongoDB. It provides examples of flexible data models in MongoDB and how it enables high data throughput, handling of big data, and low latency. Specific use cases mentioned include high volume data feeds, operational intelligence, behavioral profiles, product catalogues, content management, and metadata. MongoDB is presented as a good fit for applications that involve large numbers of objects, high read/write throughput, low latency needs, variable data in objects, and cloud-based deployment.
Webinar: Practical use-cases to monetize Open Banking APIsShubaS4
In this webinar, Thomas Zink – IDC research director for European financial services talked about the revenue potential of API enabled use-cases and how to overcome barriers to adoption. Karthik TS – Head, CoE, Torry Harris detailed the best practices to productize APIs, effective API management and marketplace-banking solutions.
This document provides an overview of research methodology for a project report on factors behind the use of plastic money. It includes an introduction to plastic money and its history from the 1900s to present day. The history section discusses the development of early charge cards, credit cards like Diners Club, and the introduction of technologies like magnetic stripes, ATMs, and chip technology. It also discusses the development of major card brands like Visa, Mastercard, and Discover. The document concludes with sections on recent mobile payment methods and an overview of the Australian banking industry and key banks.
Here's a quick overview of Credit Unions for anyone who would like to understand this type of financial services firm. Member owned, with products that are typical to banks. Perfect introduction to use for new customers. We can add to or modify this presentation for any credit union that would like a custom version for educating community groups. Contact us 316 680 6482.
Chase Bank has developed a two-phase digital strategy to increase brand awareness and drive more traffic to their mobile app and bank locations. Phase one focuses on greater mobile brand awareness, increasing mobile app visitors, and driving more traffic into Chase Bank. The target audience and main competitors are also identified. Phase two will develop the strategic vision and implementation tactics through a SWOT analysis and optimized creative platform, with measurement of results through marketing automation and app usage and cross-channel interactions.
Using Mobile Money to Promote Financial Inclusion in PakistanKarandaaz Pakistan
This work provides an overview of the state of financial inclusion in Pakistan along with the mobile financial services industry, and points to specific opportunities which, if capitalized upon, could improve m-wallet uptake. Published for the first time in Pakistan, the deck brings together information from both national and international data sets and reports on financial inclusion and mobile money.
The journey from open banking to open finance+. The evolution of open banking based on API as of now and where it could go from here. Risks and opportunities for market participants.
Financial analysis of yes bank by Saurabh Kumar +91 9990415104Saurabh Kumar
- YES Bank is a private bank founded in 2004 that has expanded rapidly, with the number of offices increasing from 118 to 428 and number of employees increasing from 2671 to 7024 from 2008-2013.
- Financially, deposits increased 76% while investments and interest income increased around 84% and 76% respectively over the five-year period. Return on equity rose 27% showing increased profits.
- Compared to other private and commercial banks, YES Bank has increased the number of offices and business per employee at a higher rate but has a lower increase in number of employees, possibly to control costs.
This document discusses MongoDB and common use cases for NoSQL databases and MongoDB. It provides examples of flexible data models in MongoDB and how it enables high data throughput, handling of big data, and low latency. Specific use cases mentioned include high volume data feeds, operational intelligence, behavioral profiles, product catalogues, content management, and metadata. MongoDB is presented as a good fit for applications that involve large numbers of objects, high read/write throughput, low latency needs, variable data in objects, and cloud-based deployment.
Webinar: Practical use-cases to monetize Open Banking APIsShubaS4
In this webinar, Thomas Zink – IDC research director for European financial services talked about the revenue potential of API enabled use-cases and how to overcome barriers to adoption. Karthik TS – Head, CoE, Torry Harris detailed the best practices to productize APIs, effective API management and marketplace-banking solutions.
This document provides an overview of research methodology for a project report on factors behind the use of plastic money. It includes an introduction to plastic money and its history from the 1900s to present day. The history section discusses the development of early charge cards, credit cards like Diners Club, and the introduction of technologies like magnetic stripes, ATMs, and chip technology. It also discusses the development of major card brands like Visa, Mastercard, and Discover. The document concludes with sections on recent mobile payment methods and an overview of the Australian banking industry and key banks.
Here's a quick overview of Credit Unions for anyone who would like to understand this type of financial services firm. Member owned, with products that are typical to banks. Perfect introduction to use for new customers. We can add to or modify this presentation for any credit union that would like a custom version for educating community groups. Contact us 316 680 6482.
Chase Bank has developed a two-phase digital strategy to increase brand awareness and drive more traffic to their mobile app and bank locations. Phase one focuses on greater mobile brand awareness, increasing mobile app visitors, and driving more traffic into Chase Bank. The target audience and main competitors are also identified. Phase two will develop the strategic vision and implementation tactics through a SWOT analysis and optimized creative platform, with measurement of results through marketing automation and app usage and cross-channel interactions.
Using Mobile Money to Promote Financial Inclusion in PakistanKarandaaz Pakistan
This work provides an overview of the state of financial inclusion in Pakistan along with the mobile financial services industry, and points to specific opportunities which, if capitalized upon, could improve m-wallet uptake. Published for the first time in Pakistan, the deck brings together information from both national and international data sets and reports on financial inclusion and mobile money.
The journey from open banking to open finance+. The evolution of open banking based on API as of now and where it could go from here. Risks and opportunities for market participants.
A credit bureau is an organization that collects and maintains credit information on individuals and businesses. It provides credit scores and reports that tell lenders about the risk level of a potential client so they can determine whether to approve loans and at what interest rates. The information helps lenders make informed decisions, and having a good credit history allows individuals and businesses to access more affordable credit options.
SKS is a microfinance institution founded in 1997 that provides financial services to economically weaker sections across 19 Indian states. It follows the joint liability group model, serving over 5 million borrowers through more than 1000 branches. SKS aims to financially include 50 million households across India through products like income generation loans, gold loans, and insurance. It has transformed operations with digital technologies and lowered interest rates to 19.75% to better serve customers according to its mission and vision of ethical, customer-centric services. However, SKS has also faced controversies regarding over-indebtedness, high staff turnover, and corporate governance issues.
What is Predictive Analytics?
Predictive Analytics is the stream of the advanced analytics which utilizes diverse techniques like data mining, predictive modelling, statistics, machine learning and artificial intelligence to analyse current data and predict future.
To Know more: https://goo.gl/zAcnCR
LOAN DEFAULT PREDICTION – A CASE STUDY
Content Covered in this video:
Business Problem & Benefits
The Risk - LOAN DEFAULT PREDICTION
Data Analysis Process
Data Processing
Predictive Analysis Process
Tools & Technology
This document discusses microfinance and building a sustainable microfinance sector in India. It begins by defining microfinance and outlining its current reach in India. It then discusses challenges like high operating costs due to low transaction values, geographic spread, and lack of infrastructure. The document proposes a three-track approach using existing financial institutions, new microfinance institutions, and community-based organizations. It examines multiple dimensions of sustainability and suggests legal and regulatory changes to promote sustainable microfinance institutions in India.
SME neo-banks are digital banks that offer business banking services for small businesses. They have seen growth globally but are most mature in Europe and China. Chinese neo-banks are dominated by large tech companies while other regions have more independent and bank-owned neo-banks. Common services include business accounts, loans, cards and integrated accounting/invoicing. Neo-banks use subscription-based models and generate revenue from transactions, deposits and subscriptions. They have much higher customer-to-employee ratios than traditional banks due to efficient digital operations. India is seeing increased focus on SMEs and has potential for neo-bank growth given the large unmet credit needs of its over 60 million SMEs.
Bancassurance refers to the distribution of insurance products through bank distribution channels. The key factors for the successful sale of life insurance policies through banking networks include the market image and perception of banks in a given market, a legal framework that allows for bancassurance, and exploiting an integrated management model between banks and insurers. An integrated model allows for a comprehensive view of customer needs, quick sales and contract issuance, and decentralized decision making to speed up the process.
''Qualtech consultants'', An ISO 9001:2008 certified as a leading professionally managed Business & Technolog solution company operating out of New Delhi, india engaged in the business of poroviding array of Business & technology solutions & ervices across Domains & platforms like Microsoft(Dot Net, VB, ASP), LAMP, RoR, Oracle, mySQL, MSSQL on Windows/Linux/Unix and AIX environments, Mobile Engineering etc for clients worldwide.
This document provides an overview of credit ratings and rating agencies in India. It discusses the four main types of investors, the role of rating agencies in providing guidance to investors with money, and defines what a credit rating is. It also summarizes the key factors considered in credit ratings like management quality, earnings prospects, financial strength, and risk. Rating agencies ensure dependability through independence and collective judgment. The document provides examples of rating scales and factors considered for bank ratings and equity assessments.
Personal SWOT analysis & SWOT analysis of Kotak Mahindra BankJiniaBanerjee1
This document contains a personal SWOT analysis conducted by Jinia Banerjee, a recent graduate from Calcutta University pursuing a PGDBM. It outlines her strengths such as willingness to learn and leadership skills, weaknesses like impatience, and opportunities provided by her program. Potential threats include lack of work experience and industry competition. She plans to enhance her skills, take advantage of opportunities, and minimize threats to achieve career success.
GoLismero is an open source framework for security testing web applications. It allows scanning websites to identify vulnerabilities and integrates with tools like sqlmap, xsser, openvas. Key features include platform independence, no native dependencies, good performance, and easy plugin development. Commands allow scanning targets and generating reports from the scan database. Upcoming features will integrate with more security tools and provide a web UI and additional report formats.
Axis Bank is one of India's largest private sector banks. It was founded in 1993 and started operations in 1994. Headquartered in Mumbai, Axis Bank has over 4,200 branches across India and international offices in Singapore, Hong Kong, Dubai, Colombo, Abu Dhabi, and the UK. The bank reported revenues of $5.3 billion for the 2014-2015 fiscal year with a net income of $820 million. Axis Bank focuses on retail banking, corporate banking, investment banking, and other services.
Kakao Bank - Trailblazing Neobank from South KoreaSam Ghosh
Kakao Bank was launched in the year 2017 as part of the Kakao Corp. Within 24 hours, Kakao Bank enrolled 300K subscribers, 2 million in 15 days. As of the end of 2020, this South Korean Bank had more than 13 million users, around a quarter of the South Korean population. The bank has reached a loan book size of 20.3 trillion KRW (US$17.94 billion). The operating income for the bank stood at 804 billion KRW (~US$708 million) with 113.6 billion KRW (~US$100 million) net profit in FY2020.
Just after 3 years of its launched Kakao Bank is already planning IPO and is valued at around 10 trillion won (US$9.15 billion).
Let us learn about Kakao Bank.
Exploratory Data Analysis For Credit Risk AssesmentVishalPatil527
This document presents an analysis of credit risk for a bank. It aims to identify patterns that indicate if a client will have difficulty paying installments. The analysis includes:
- Cleaning and merging loan application and previous loan data
- Analyzing relationships between client attributes and payment difficulties through visualization
- Key insights show strong indicators of default include clients with certain housing types, family statuses, occupations or lower education levels. Clients with higher incomes, providing more documents, or older ages are less likely to default. Based on these insights, a credit scoring system is proposed to help the bank make lending decisions.
Digitalization of banking refers to conducting its existing operations and developing new functions connecting to Banking through Digital Mediums. The presentation has attempted to explore the Digitalization process in Banking industry that took place from the date of independence to till 2018. The presentation invites any constructive criticism or remarks for future improvement.
The document discusses the development of a credit default prediction model called Def_Catch using machine learning algorithms. Def_Catch was trained on a dataset of 100,000 examples with 11 attributes related to borrowers' credit histories and demographics. Random forest achieved the highest accuracy of 93.14% at predicting which borrowers would default in the next 2 years, outperforming logistic regression, naive bayes, decision trees, and multi-layer perceptron models. The top predictors of default included credit utilization, age, number of late payments, debt ratio, and income. Def_Catch provides insights into borrower risk that are difficult to discern from raw data alone.
This document discusses open banking and its implications for Suryoday Small Finance Bank. It begins with an overview of open banking, including how it creates an open ecosystem compared to traditional closed systems. It then covers the key drivers of open banking such as regulations, emerging technologies, competition, and consumer demand. Regulatory timelines for open banking in various regions are provided. The document discusses how banks can take different approaches to open banking, and what opportunities it provides for Suryoday, including providing invoices and payments services for small businesses. It concludes that Suryoday is well positioned to become an open bank due to its existing API-based systems and platform approach.
Canara Bank engages in corporate social responsibility through various programs. It spends 10 crore annually on CSR initiatives and each employee donates 3 rupees per month to social causes. The bank's founding principles focus on education, removing ignorance, and serving communities. Canara Bank has over 2,400 branches and provides various technological services. It operates training programs, water and rural development projects, and addresses customer and shareholder complaints.
A credit bureau is an organization that collects and maintains credit information on individuals and businesses. It provides credit scores and reports that tell lenders about the risk level of a potential client so they can determine whether to approve loans and at what interest rates. The information helps lenders make informed decisions, and having a good credit history allows individuals and businesses to access more affordable credit options.
SKS is a microfinance institution founded in 1997 that provides financial services to economically weaker sections across 19 Indian states. It follows the joint liability group model, serving over 5 million borrowers through more than 1000 branches. SKS aims to financially include 50 million households across India through products like income generation loans, gold loans, and insurance. It has transformed operations with digital technologies and lowered interest rates to 19.75% to better serve customers according to its mission and vision of ethical, customer-centric services. However, SKS has also faced controversies regarding over-indebtedness, high staff turnover, and corporate governance issues.
What is Predictive Analytics?
Predictive Analytics is the stream of the advanced analytics which utilizes diverse techniques like data mining, predictive modelling, statistics, machine learning and artificial intelligence to analyse current data and predict future.
To Know more: https://goo.gl/zAcnCR
LOAN DEFAULT PREDICTION – A CASE STUDY
Content Covered in this video:
Business Problem & Benefits
The Risk - LOAN DEFAULT PREDICTION
Data Analysis Process
Data Processing
Predictive Analysis Process
Tools & Technology
This document discusses microfinance and building a sustainable microfinance sector in India. It begins by defining microfinance and outlining its current reach in India. It then discusses challenges like high operating costs due to low transaction values, geographic spread, and lack of infrastructure. The document proposes a three-track approach using existing financial institutions, new microfinance institutions, and community-based organizations. It examines multiple dimensions of sustainability and suggests legal and regulatory changes to promote sustainable microfinance institutions in India.
SME neo-banks are digital banks that offer business banking services for small businesses. They have seen growth globally but are most mature in Europe and China. Chinese neo-banks are dominated by large tech companies while other regions have more independent and bank-owned neo-banks. Common services include business accounts, loans, cards and integrated accounting/invoicing. Neo-banks use subscription-based models and generate revenue from transactions, deposits and subscriptions. They have much higher customer-to-employee ratios than traditional banks due to efficient digital operations. India is seeing increased focus on SMEs and has potential for neo-bank growth given the large unmet credit needs of its over 60 million SMEs.
Bancassurance refers to the distribution of insurance products through bank distribution channels. The key factors for the successful sale of life insurance policies through banking networks include the market image and perception of banks in a given market, a legal framework that allows for bancassurance, and exploiting an integrated management model between banks and insurers. An integrated model allows for a comprehensive view of customer needs, quick sales and contract issuance, and decentralized decision making to speed up the process.
''Qualtech consultants'', An ISO 9001:2008 certified as a leading professionally managed Business & Technolog solution company operating out of New Delhi, india engaged in the business of poroviding array of Business & technology solutions & ervices across Domains & platforms like Microsoft(Dot Net, VB, ASP), LAMP, RoR, Oracle, mySQL, MSSQL on Windows/Linux/Unix and AIX environments, Mobile Engineering etc for clients worldwide.
This document provides an overview of credit ratings and rating agencies in India. It discusses the four main types of investors, the role of rating agencies in providing guidance to investors with money, and defines what a credit rating is. It also summarizes the key factors considered in credit ratings like management quality, earnings prospects, financial strength, and risk. Rating agencies ensure dependability through independence and collective judgment. The document provides examples of rating scales and factors considered for bank ratings and equity assessments.
Personal SWOT analysis & SWOT analysis of Kotak Mahindra BankJiniaBanerjee1
This document contains a personal SWOT analysis conducted by Jinia Banerjee, a recent graduate from Calcutta University pursuing a PGDBM. It outlines her strengths such as willingness to learn and leadership skills, weaknesses like impatience, and opportunities provided by her program. Potential threats include lack of work experience and industry competition. She plans to enhance her skills, take advantage of opportunities, and minimize threats to achieve career success.
GoLismero is an open source framework for security testing web applications. It allows scanning websites to identify vulnerabilities and integrates with tools like sqlmap, xsser, openvas. Key features include platform independence, no native dependencies, good performance, and easy plugin development. Commands allow scanning targets and generating reports from the scan database. Upcoming features will integrate with more security tools and provide a web UI and additional report formats.
Axis Bank is one of India's largest private sector banks. It was founded in 1993 and started operations in 1994. Headquartered in Mumbai, Axis Bank has over 4,200 branches across India and international offices in Singapore, Hong Kong, Dubai, Colombo, Abu Dhabi, and the UK. The bank reported revenues of $5.3 billion for the 2014-2015 fiscal year with a net income of $820 million. Axis Bank focuses on retail banking, corporate banking, investment banking, and other services.
Kakao Bank - Trailblazing Neobank from South KoreaSam Ghosh
Kakao Bank was launched in the year 2017 as part of the Kakao Corp. Within 24 hours, Kakao Bank enrolled 300K subscribers, 2 million in 15 days. As of the end of 2020, this South Korean Bank had more than 13 million users, around a quarter of the South Korean population. The bank has reached a loan book size of 20.3 trillion KRW (US$17.94 billion). The operating income for the bank stood at 804 billion KRW (~US$708 million) with 113.6 billion KRW (~US$100 million) net profit in FY2020.
Just after 3 years of its launched Kakao Bank is already planning IPO and is valued at around 10 trillion won (US$9.15 billion).
Let us learn about Kakao Bank.
Exploratory Data Analysis For Credit Risk AssesmentVishalPatil527
This document presents an analysis of credit risk for a bank. It aims to identify patterns that indicate if a client will have difficulty paying installments. The analysis includes:
- Cleaning and merging loan application and previous loan data
- Analyzing relationships between client attributes and payment difficulties through visualization
- Key insights show strong indicators of default include clients with certain housing types, family statuses, occupations or lower education levels. Clients with higher incomes, providing more documents, or older ages are less likely to default. Based on these insights, a credit scoring system is proposed to help the bank make lending decisions.
Digitalization of banking refers to conducting its existing operations and developing new functions connecting to Banking through Digital Mediums. The presentation has attempted to explore the Digitalization process in Banking industry that took place from the date of independence to till 2018. The presentation invites any constructive criticism or remarks for future improvement.
The document discusses the development of a credit default prediction model called Def_Catch using machine learning algorithms. Def_Catch was trained on a dataset of 100,000 examples with 11 attributes related to borrowers' credit histories and demographics. Random forest achieved the highest accuracy of 93.14% at predicting which borrowers would default in the next 2 years, outperforming logistic regression, naive bayes, decision trees, and multi-layer perceptron models. The top predictors of default included credit utilization, age, number of late payments, debt ratio, and income. Def_Catch provides insights into borrower risk that are difficult to discern from raw data alone.
This document discusses open banking and its implications for Suryoday Small Finance Bank. It begins with an overview of open banking, including how it creates an open ecosystem compared to traditional closed systems. It then covers the key drivers of open banking such as regulations, emerging technologies, competition, and consumer demand. Regulatory timelines for open banking in various regions are provided. The document discusses how banks can take different approaches to open banking, and what opportunities it provides for Suryoday, including providing invoices and payments services for small businesses. It concludes that Suryoday is well positioned to become an open bank due to its existing API-based systems and platform approach.
Canara Bank engages in corporate social responsibility through various programs. It spends 10 crore annually on CSR initiatives and each employee donates 3 rupees per month to social causes. The bank's founding principles focus on education, removing ignorance, and serving communities. Canara Bank has over 2,400 branches and provides various technological services. It operates training programs, water and rural development projects, and addresses customer and shareholder complaints.
Программные продукты БИТ.ФИНАСН - это специализированная линейка удобных и надежных инструментов планирования и контроля финансовых средств и денежных потоков компаниия, бюджетирования деятельности и план-факторного анализа, ведения управленческого учета.
1С: Бухгалтерия некредитной финансовой организации. Ivan Pervobitov
https://www.1cbit.ru/1csoft/1c-bukhgalteriya-nekreditnoy-finansovoy-organizatsii-korp/
Переходите по ссылке и узнайте больше об 1С: "Бухгалтерия некредитной финансовой организации" на сайте компании "Первый БИТ"!
Отраслевое решение 1С: "Бухгалтерия некредитной финансовой организации" предназначено для автоматизации бухгалтерского и налогового учета, включая подготовку регламентированной отчетности, в некредитных финансовых организациях.
Решение автоматизирует учет:
-основных средств, нематериальных активов, запасов;
-денежных средств, материальных ценностей;
-взаиморасчетов с контрагентами, расчетов с подотчетными лицами;
-доходов и расходов, полученных и уплаченных авансов;
-НДС;
-средств и предметов труда, назначение которых не определено, полученных по договорам отступного, залога, в связи с отказом страхователя (выгодоприобретателя) от права собственности на застрахованное имущество.
Компания "Первый БИТ" является крупнейшей компанией-франчайзи 1С. Компания работает на рынке более 20 лет и насчитывает 80 офисов в 49 городах России, благодаря чему она является крупнейшей региональной сетью среди фирм-франчайзи 1С в России.
2. Цели и формат встречи
Обзор реализованных проектов
◦ Программный комплекс “IFRS::PROCESS” (на базе “CS::BI” и АБС “Б2”)
◦ Подсистема “Формирование резервов МСФО” (АБС “Б2 Молдова”)
Обсуждения:
◦ Совместимость и необходимость автоматизации требований по
формированию резервов Basel, МСФО, Постановления НБУ №23
◦ Перспективные направления развития автоматизации риск
менеджмента кредитного портфеля:
Расчет показателей: стоимости риска (cost of risk), VaR, отчеты по
ликвидности, GAP, экономического капитала
Анализ данных с помощью “CS::Business Intelligence” (на базе Oracle BI)
Идея создания ПО: “Разработка системы внутренних кредитных рейтингов”
ПЛАН
1
5. РЕТРОСПЕКТИВНЫЙ АНАЛИЗ
РАСЧЕТ DPD ПО МЕТОДУ FIFO
РАСЧЕТ ПОКАЗАТЕЛЕЙ СОГЛАСНО БАЗЕЛЯ:
◦ PD (Probability of Default) - cреднегодовая вероятность
дефолта заемщика;
◦ LGD (Loss Given Default) - среднеожидаемая доля потерь
средств в случае дефолта;
◦ EAD (Exposure at Default) - величина средств под риском
◦ GRP (Group) – групповая принадлежность компании-
заемщика.
РАСЧЕТ ПОКАЗАТЕЛЕЙ СОГЛАСНО МСФО:
◦ портфельные расчеты:
PD (прогноз перехода в DPD 90+)
LGD
4
АНАЛИЗ ТРЕБОВАНИЙ.
6. АБС “Б2”
Подсистема “Управленческий учет(ISMA)”
Сторонняя система БПК ( “IS-CARD”, “Trans
Master” )
Хранилище разработанное CS “B2_OLAP” +
модель данных + Business Intelligence
Система Oracle BI = “CS::BI”
АБС “Б2 Молдова”
ИНФОРМАЦИОННОЕ ОКРУЖЕНИЕ
5
13. Расчет PD ( на основе статистики )
Расчет LGD ( на основе статистики )
Расчет DPD (на основе всех проводок
по погашению, просрочке, начислению)
Расчет Exposure ( с учетом stop
accrual 90+)
Расчеты показателей по портфелям однородных ссуд
11
14. В Феврале клиент заплатил 8.000
грн.
1 Янв 1 Фев 1 Maр 1 Апр 1 Май
500
2.000
500
4.500
2.500
Просрочка - 2.000 грн.
28 Фев клиент в 1-30
DPD
Тело
Проценты
Вертикальный
500
1 Янв 1 Фев 1 Мар 1 Апр 1 Май
Просрочка – 2.000 грн.
28 Фев клиент в 31-60
DPD
Тело
Проценты
Горизонтальный
500
1.000
3.500
1.000
3.500
Расчет DPD
Пример:
500 График платежа
1 Янв 1 Фев 1 Мар 1 Апр 1 Май
4.500
500
4.500
500
4.500
500
4.500
500
4.500
… и т.д.
4.500
12
15. Начисленные, но неуплаченные проценты на дату ПБЗ,
рассчитанные на отчетную дату = 10 грн.
EXPOSURE= Z + 5*100 + 10 = Z + 510
Начисленные, но неуплаченные проценты на дату ПБЗ,
рассчитанные на отчетную дату = 10 грн.
EXPOSURE= Z + 4*100 + 110 – (30+20) = Z + 460
Расчет DPD и EaD
13
16. Кредиты, по которым действуют постоянные решения по реструктуризации
долга, «удерживают» на протяжении 3 месяцев после проведения
реструктуризации в последней до момента реструктуризации ступени
просрочки. Если по истечении 3 месяцев после реструктуризации все 3
последних регулярных платежа были погашены и отсутствует просрочка по
кредиту, его учитывают на 0 ступени просрочки (без просрочки). Если по
кредиту существует просрочка, его относят к ступени не ниже, чем та, где
кредит находился до реструктуризации, с учетом дополнительно
накопившейся просрочки
Расчет DPD
14
21. Расчет кредитов коллектив – корпоративный блок
,
PD – по матрице миграции (рейтинги), усредненной за 12 месяцев
LGD - рассчитывается исходя из структуры покрытия обеспечением,
ликвидности обеспечения и затрат на реализацию
16
35. Общие моменты и отличия МСФО и Базель
"потери по кредитам"
Var и CaR
методолгия
Расчеты резервов
согласно МСФО 30
36. № Наименование Базель МСФО
1. Цель Расчет капитала для
покрытия непредвиденных
потерь и ожидаемых, под
которые сформировано
недостаточно резервов
Расчет понесенных потерь
исходя из выявленных
признаков обесценения по
портфелю ссуд или по
конкретной сделке
2. Модель оценки Модель ожидаемых убытков
для отдельных активов
Модель понесенных
потерь для отдельных
активов и групп
финансовых активов
3. Формула расчета PD*LGD*EAD PD*LGD*LiP*EAD, EAD –
RCF, PR*EAD
4. Активы под риском Кредитный портфель,
торговый портфель
Кредитный портфель
5. Факторы риска Событие дефолта заемщика Свидетельства
обесценения и тест на
обесценение
6. Параметры моделей AIRB подход PD, LGD на
основании моделей
внутренних рейтингов и
FLOW rate анализа
PD, LGD на основании
моделей Базель-2, LIP на
основании собственной
или среднерыночной
статики
Сравнение МСФО и Базель2
31
37. 1. Единая база данных: активы, контрагенты, счета и документы –
упрощают процедуру анализа необходимых показателей для
Базель 2 и МСФО и расчета итоговых величин.
2. Возможность использования моделей расчета Базель 2 для оценки
понесенных потерь согласно МСФО и оценки активов по
справедливой стоимости и соответственно практическое
обоснование полученных результатов.
3. Построение целостной системы риск-менеджменты, начиная от
этапов рейтингования и ранжирования заемщиков и кредитного
портфеля и заканчивая анализом понесенных, ожидаемых и
непредвиденных потерь, а также взвешенных по риску активов.
4. Экономия ресурсов на установке, контроле и мониторинге
смежных признаков обесценения и событий дефолта заемщика.
5. Возможность построения управленческих отчетов разной
структуры для принятия решений.
6. Использование смежных результатов расчета для анализа
качества активов их эффективности и использования для
дальнейшего ценообразования.
Возможности совмещения задач по МСФО и Базель II
32
38. МСФО, Базель II, Постановление НБУ №23
На наш взгляд, архитектура расчетов на
будущее:
Расчет резервов по Постановлению № 23 в
АБС “Б2”
Отдельное ПО по расчету резервов
согласно требований МСФО+Базель на
базе хранилища
33
39. Рассчитываете ли резервы согласно
требований МСФО ( покрытие Expected
Losses) ?
Для расчета PD, LGD, EAD применяете ли
математические модели ?
Какое ПО применяете ?
Делаете ли вы расчеты потерь согласно МСФО и Базель II ?
34
45. Варианты реализации проекта с нашим участием
ВАРИАНТ 1. Внедряете западное решение для расчетов
на базе хранилища CS::BI и
для анализа данных тоже применяем CS::BI
ВАРИАНТ 2. CS выполняет разработку расчетного блока
на базе хранилища CS::BI и
для анализа данных тоже применяем CS::BI
40
46. Модели ранжирования Преимущества Недостатки
Экспертные Простота
Понятность
Не учитывает статистику
Статистические Строится на
реальных
статистических
данных
Требуют пересмотра при
обновлении статистики,
требуют достаточности
статистических данных
Смешанные Включают преимущества и недостатки
экспертных и статистических моделей
Нейронных сетей Неплохой
результат
применения
Непрозрачность
Виды моделей рейтингования:
41
47. БД дефолтов
Экспертное определение набора финансовых
показателей
Выбор
значимых
показателей
Определение весов
параметров и построение
модели
П о с т р о е н и е м о д е л и
Оценка
устойчивости
коэффициентов
регрессии
В а л и д а ц и я м о д е л и
Модель рейтингования
Бэк-тестинг всей модели
и отдельных параметров
БД Фин.
отчетности
заемщиков (есть в
АБС Б2)
Статистические
расчеты
Доработка
модели
Тестирование на выборках
out-of sample и out-of-time
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48. Этапы построения:
1. Экспертный выбор списка коэффициентов, из которого выбираем
наиболее значимые с точки зрения «предсказания» будущего дефолта.
2. Анализ значимости показателей (методом Information value).
3. Анализ корреляции показателей. Выбор показателей из перечня
зависимых.
4. Формирование «обучающей» выборки. Обучающая выборка – это
выборка клиентов на данных которых строится модель ранжирования.
5. Анализ стабильности коэффициентов в уравнении регрессии
логарифма шансов попадания заемщика в дефолт (логит-регрессия).
6. Определение балльных оценок в разрезе показателей и их дальнейшее
сглаживание.
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49. Методы оценки качества: Accuracy Ratio
Accuracy ratio (Коэффициент Gini) – мера предсказательной силы модели рейтингования. Определяется, как
соотношение площадей под идеально разделяющей кривой и кривой разделяющий на основе построенной
модели. Для равномерного распределения он равен нулю, для абсолютного неравенства он равен единице.
Высокий уровень коэффициента говорит о значительной неравномерности распределения дефолтов, и,
следовательно, о хорошей предсказательной силе модели.
Таблица 1. Качество рейтинговых систем
Интервал
Accuracy Ratio
Качество модели
60-80% Очень хорошее
40-60% Хорошее
20-40% Среднее
Ниже 20% Низкое
)(
)(
EDAS
ECAS
AR
1
Долякомпанийпопавшихвдефолтв
течениезаданногосрока
Доля всех компаний
1
Модель
рейтингования
Случайная
модель
Идеальная
модель
E
D A
С
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Пройдемся по двум обсуждениям а затем зададим вам вопросы и предложим пути автоматизаци
Симуляции Монте-Карло случайных величин, характеризующих активы компании и испытывающих марковские взаимозависимые переходы между кредитными рейтингами заемщиков (возможных значений времен дефолта по специальному алгоритму), от которых зависит рыночная приведенная стоимость (PV, present value) кредитных продуктов. Кривая потерь строится по значениям возможных PV портфеля после значительного числа симуляций (модель CreditMetrics)