The Top 10  Most Difficult Countries for Identity Verification

The Top 10  Most Difficult Countries for Identity Verification

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    • Saudi Arabia (‫المملكة العربية السعودية‬‎)+966
    • United Arab Emirates (‫الإمارات العربية المتحدة‬‎)+971
    • Qatar (‫قطر‬‎)+974
    • Oman (‫عُمان‬‎)+968
    • Bahrain (‫البحرين‬‎)+973
    • Kuwait (‫الكويت‬‎)+965
    • Morocco (‫المغرب‬‎)+212
    • Afghanistan (‫افغانستان‬‎)+93
    • Albania (Shqipëri)+355
    • Algeria (‫الجزائر‬‎)+213
    • American Samoa+1684
    • Andorra+376
    • Angola+244
    • Anguilla+1264
    • Antigua and Barbuda+1268
    • Argentina+54
    • Armenia (Հայաստան)+374
    • Aruba+297
    • Australia+61
    • Austria (Österreich)+43
    • Azerbaijan (Azərbaycan)+994
    • Bahamas+1242
    • Bahrain (‫البحرين‬‎)+973
    • Bangladesh (বাংলাদেশ)+880
    • Barbados+1246
    • Belarus (Беларусь)+375
    • Belgium (België)+32
    • Belize+501
    • Benin (Bénin)+229
    • Bermuda+1441
    • Bhutan (འབྲུག)+975
    • Bolivia+591
    • Bosnia and Herzegovina (Босна и Херцеговина)+387
    • Botswana+267
    • Brazil (Brasil)+55
    • British Indian Ocean Territory+246
    • British Virgin Islands+1284
    • Brunei+673
    • Bulgaria (България)+359
    • Burkina Faso+226
    • Burundi (Uburundi)+257
    • Cambodia (កម្ពុជា)+855
    • Cameroon (Cameroun)+237
    • Canada+1
    • Cape Verde (Kabu Verdi)+238
    • Caribbean Netherlands+599
    • Cayman Islands+1345
    • Central African Republic (République centrafricaine)+236
    • Chad (Tchad)+235
    • Chile+56
    • China (中国)+86
    • Christmas Island+61
    • Cocos (Keeling) Islands+61
    • Colombia+57
    • Comoros (‫جزر القمر‬‎)+269
    • Congo (DRC) (Jamhuri ya Kidemokrasia ya Kongo)+243
    • Congo (Republic) (Congo-Brazzaville)+242
    • Cook Islands+682
    • Costa Rica+506
    • Côte d’Ivoire+225
    • Croatia (Hrvatska)+385
    • Cuba+53
    • Curaçao+599
    • Cyprus (Κύπρος)+357
    • Czech Republic (Česká republika)+420
    • Denmark (Danmark)+45
    • Djibouti+253
    • Dominica+1767
    • Dominican Republic (República Dominicana)+1
    • Ecuador+593
    • Egypt (‫مصر‬‎)+20
    • El Salvador+503
    • Equatorial Guinea (Guinea Ecuatorial)+240
    • Eritrea+291
    • Estonia (Eesti)+372
    • Ethiopia+251
    • Falkland Islands (Islas Malvinas)+500
    • Faroe Islands (Føroyar)+298
    • Fiji+679
    • Finland (Suomi)+358
    • France+33
    • French Guiana (Guyane française)+594
    • French Polynesia (Polynésie française)+689
    • Gabon+241
    • Gambia+220
    • Georgia (საქართველო)+995
    • Germany (Deutschland)+49
    • Ghana (Gaana)+233
    • Gibraltar+350
    • Greece (Ελλάδα)+30
    • Greenland (Kalaallit Nunaat)+299
    • Grenada+1473
    • Guadeloupe+590
    • Guam+1671
    • Guatemala+502
    • Guernsey+44
    • Guinea (Guinée)+224
    • Guinea-Bissau (Guiné Bissau)+245
    • Guyana+592
    • Haiti+509
    • Honduras+504
    • Hong Kong (香港)+852
    • Hungary (Magyarország)+36
    • Iceland (Ísland)+354
    • India (भारत)+91
    • Indonesia+62
    • Iran (‫ایران‬‎)+98
    • Iraq (‫العراق‬‎)+964
    • Ireland+353
    • Isle of Man+44
    • Israel (‫ישראל‬‎)+972
    • Italy (Italia)+39
    • Jamaica+1876
    • Japan (日本)+81
    • Jersey+44
    • Jordan (‫الأردن‬‎)+962
    • Kazakhstan (Казахстан)+7
    • Kenya+254
    • Kiribati+686
    • Kosovo+383
    • Kuwait (‫الكويت‬‎)+965
    • Kyrgyzstan (Кыргызстан)+996
    • Laos (ລາວ)+856
    • Latvia (Latvija)+371
    • Lebanon (‫لبنان‬‎)+961
    • Lesotho+266
    • Liberia+231
    • Libya (‫ليبيا‬‎)+218
    • Liechtenstein+423
    • Lithuania (Lietuva)+370
    • Luxembourg+352
    • Macau (澳門)+853
    • Macedonia (FYROM) (Македонија)+389
    • Madagascar (Madagasikara)+261
    • Malawi+265
    • Malaysia+60
    • Maldives+960
    • Mali+223
    • Malta+356
    • Marshall Islands+692
    • Martinique+596
    • Mauritania (‫موريتانيا‬‎)+222
    • Mauritius (Moris)+230
    • Mayotte+262
    • Mexico (México)+52
    • Micronesia+691
    • Moldova (Republica Moldova)+373
    • Monaco+377
    • Mongolia (Монгол)+976
    • Montenegro (Crna Gora)+382
    • Montserrat+1664
    • Morocco (‫المغرب‬‎)+212
    • Mozambique (Moçambique)+258
    • Myanmar (Burma) (မြန်မာ)+95
    • Namibia (Namibië)+264
    • Nauru+674
    • Nepal (नेपाल)+977
    • Netherlands (Nederland)+31
    • New Caledonia (Nouvelle-Calédonie)+687
    • New Zealand+64
    • Nicaragua+505
    • Niger (Nijar)+227
    • Nigeria+234
    • Niue+683
    • Norfolk Island+672
    • North Korea (조선 민주주의 인민 공화국)+850
    • Northern Mariana Islands+1670
    • Norway (Norge)+47
    • Oman (‫عُمان‬‎)+968
    • Pakistan (‫پاکستان‬‎)+92
    • Palau+680
    • Palestine (‫فلسطين‬‎)+970
    • Panama (Panamá)+507
    • Papua New Guinea+675
    • Paraguay+595
    • Peru (Perú)+51
    • Philippines+63
    • Poland (Polska)+48
    • Portugal+351
    • Puerto Rico+1
    • Qatar (‫قطر‬‎)+974
    • Réunion (La Réunion)+262
    • Romania (România)+40
    • Russia (Россия)+7
    • Rwanda+250
    • Saint Barthélemy (Saint-Barthélemy)+590
    • Saint Helena+290
    • Saint Kitts and Nevis+1869
    • Saint Lucia+1758
    • Saint Martin (Saint-Martin (partie française))+590
    • Saint Pierre and Miquelon (Saint-Pierre-et-Miquelon)+508
    • Saint Vincent and the Grenadines+1784
    • Samoa+685
    • San Marino+378
    • São Tomé and Príncipe (São Tomé e Príncipe)+239
    • Saudi Arabia (‫المملكة العربية السعودية‬‎)+966
    • Senegal (Sénégal)+221
    • Serbia (Србија)+381
    • Seychelles+248
    • Sierra Leone+232
    • Singapore+65
    • Sint Maarten+1721
    • Slovakia (Slovensko)+421
    • Slovenia (Slovenija)+386
    • Solomon Islands+677
    • Somalia (Soomaaliya)+252
    • South Africa+27
    • South Korea (대한민국)+82
    • South Sudan (‫جنوب السودان‬‎)+211
    • Spain (España)+34
    • Sri Lanka (ශ්‍රී ලංකාව)+94
    • Sudan (‫السودان‬‎)+249
    • Suriname+597
    • Svalbard and Jan Mayen+47
    • Swaziland+268
    • Sweden (Sverige)+46
    • Switzerland (Schweiz)+41
    • Syria (‫سوريا‬‎)+963
    • Taiwan (台灣)+886
    • Tajikistan+992
    • Tanzania+255
    • Thailand (ไทย)+66
    • Timor-Leste+670
    • Togo+228
    • Tokelau+690
    • Tonga+676
    • Trinidad and Tobago+1868
    • Tunisia (‫تونس‬‎)+216
    • Turkey (Türkiye)+90
    • Turkmenistan+993
    • Turks and Caicos Islands+1649
    • Tuvalu+688
    • U.S. Virgin Islands+1340
    • Uganda+256
    • Ukraine (Україна)+380
    • United Arab Emirates (‫الإمارات العربية المتحدة‬‎)+971
    • United Kingdom+44
    • United States+1
    • Uruguay+598
    • Uzbekistan (Oʻzbekiston)+998
    • Vanuatu+678
    • Vatican City (Città del Vaticano)+39
    • Venezuela+58
    • Vietnam (Việt Nam)+84
    • Wallis and Futuna+681
    • Western Sahara (‫الصحراء الغربية‬‎)+212
    • Yemen (‫اليمن‬‎)+967
    • Zambia+260
    • Zimbabwe+263
    • Åland Islands+358
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    The Role of Artificial Intelligence in the Future Of Financial Fraud Detection

    Fraud Detection

    Until a few years ago Artificial Intelligence seemed like a thing from sci-fi movies. The whole concept seemed like fiction or a far fetched dream fed by wishful thinking. Then came personal assistants like Siri, Google Assistant, Bixby, Alexa and Cortana, which made the people realise that they could have something like a Jarvis in their homes as well. However, these are just known as weak AIs. Strong AIs are theoretically able to work with human cognitive abilities. Such advancing work in the field of AI is said to achieve tremendous goals in a number of fields including revolutionising the future of fraud detection.

    Identity theft and fraud in the commercial sector has been a huge problem for nearly every merchant. Particularly since the increase in online buying of consumers, the number and types of online fraud have increased tenfolds. It is becoming an inevitable task for every business to implement productive anti-fraud solutions in their practices.

    With the increase in online trade, cybercriminals have also found more intricate ways to defraud both businesses and end consumers. As the effect of access to information across the web snowballs, online scammers find new and more advanced ways to hoodwink retailers, online or otherwise.

    Fraud in the Financial Sector

    The financial sector, however, has been the most unfortunate victim of identity theft and online frauds and scams. MacAfee reported a loss of over 600 billion dollars to the global economy due to financial and other forms of cybercrimes. From Phishing scams to good old fashioned credit card to more modern fraud schemes including social engineering, card skimming, the Banking, Financial Services and the Insurance (BFSI) sector has seen the worst cases of frauds and has consequently suffered the most tremendous losses as well.

    Read: How Augmented Intelligence is next stop in ID Verification Services?

    The finance sector is constantly looking for ways to upgrade their systems an implement fraud protection measures, while cybercriminals are as a result looking and coming up with better and more advanced ways to hack into their databases or to compromise customer data to gain access to their finances. Such schemes are now being fought with advanced technology including machine learning and artificial intelligence.

    The Market for Fraud Detection

    The market for fraud detection and prevention services has gained a lot of popularity over the years, allowing the banking and the financial sector to fight fraudulent activities in an effective way. Up until now, the banking and finance sector was only able to put up walls against scammers or implement firefighting measures in case any scams surfaced.

    In 2016 the entire market for such services was valued at 13.64 billion dollars. Better yet it is expected to cross 40 billion dollars by the year 2023. Primarily fraud prevention systems are software solutions that will provide conclusive and actionable solutions to identify frauds and block or at least limit any future occurrences.

    Artificial Intelligence and Fraud Detection

    Recently, the use of artificial intelligence in the fight against fraud has helped achieve an unprecedented level of success in detecting fraudulent activity. The idea is basically to fight fire with fire; if cybercriminals can use advanced tech to defraud the system, the same knowledge should be used to develop systems that can keep crooks out.

    The idea of implementing AI-based models is to basically detect and predict identity theft and other scams commonly used by criminals to compromise financial institutions. The concept of big data comes useful herein. Since banks and financial institutes have on hand large amounts of customer data as well as transactional data, it can be used productively to predict patterns in data and look for any irregularities. This can make financial fraud detection a much easier feat for the BFSI industry.

    Find more relevant resources:

    The Role of Artificial Intelligence in the Future Of Financial Fraud Detection

    Use of AI in Identity Verification Services

    More than that, AI can now be used to detect fraudulent pursuits by enabling businesses to implement identity verification measures to authenticate the IDs of customers. Shufti is a such an AI-based identity verification software that provides an array of Know Your Customer (KYC) services through an AI-based SaaS. It has made significant headway into developing fraud prevention techniques that enable the banking sector to put up sizable defences against online fraud and cybercrime.

    Shufti is currently providing the best identity theft protection services in the market. It provides fraud prevention services through an AI-based authentication system that verifies a user’s ID in under a minute. It is supported globally and has universal language support in its system. Not only that, its restful API enables banks, financial institutes and online retailers to integrate their web-based applications and modules without any downtime.

    Fraud detection and Artificial Intelligence together have the potential to transform the future of financial fraud detection and has already made significant headway into it.

    Disclaimer: The views and opinions expressed on this webpage or weblink are those of the author only, and are not necessarily the views or opinions of Shufti Limited. The material and information on this weblink is solely for general information purposes. You should not rely upon the material or information on the website as a basis for making any business or legal decision.

    While we endeavor to keep the information up-to-date and/or correct, we make no representations or warranties of any kind, express or implied, or for any purpose about the completeness, accuracy, reliability, suitability, or availability of the contents or information herein. Any reliance on its content is thus entirely at your own risk.

    For the avoidance of doubt, Shufti Limited will not be liable for any false, inaccurate, inappropriate, or incomplete information presented herein, and all liabilities with respect to actions taken, or not taken, based on the contents or information herein, or for any loss sustained by you as a consequence are hereby expressly disclaimed by us.

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