{"id":44397,"date":"2026-05-24T23:43:14","date_gmt":"2026-05-24T21:43:14","guid":{"rendered":"https:\/\/www.derivaty.sk\/?p=44397"},"modified":"2026-01-05T14:08:11","modified_gmt":"2026-01-05T13:08:11","slug":"bonita-a-kreditne-skore-faktory-vypocet-a-vplyv","status":"publish","type":"post","link":"https:\/\/www.autoskoly.sk\/news\/bonita-a-kreditne-skore-faktory-vypocet-a-vplyv\/","title":{"rendered":"Bonita a kreditn\u00e9 sk\u00f3re: Faktory, v\u00fdpo\u010det a vplyv"},"content":{"rendered":"

\u010co je bonita a kreditn\u00e9 sk\u00f3re<\/h2>\n

Bonita<\/strong> vyjadruje schopnos\u0165 a ochotu dl\u017en\u00edka spl\u00e1ca\u0165 svoje z\u00e1v\u00e4zky v s\u00falade s dohodnut\u00fdmi podmienkami. Je v\u00fdsledkom komplexn\u00e9ho pos\u00fadenia pr\u00edjmov, v\u00fddavkov, z\u00e1v\u00e4zkov, stability a rizikov\u00e9ho profilu \u017eiadate\u013ea. Kreditn\u00e9 sk\u00f3re<\/strong> je kvantitat\u00edvny ukazovate\u013e (typicky bodov\u00e9 hodnotenie), ktor\u00fd sumarizuje verite\u013eom odhadovan\u00e9 riziko nesplatenia do jednej \u010d\u00edselnej veli\u010diny. Sk\u00f3re sa pou\u017e\u00edva pri schva\u013eovan\u00ed, stanoven\u00ed \u00faverov\u00fdch limitov, mar\u017e\u00ed a pri riaden\u00ed portf\u00f3lia.<\/p>\n

K\u013e\u00fa\u010dov\u00e9 pojmy a metriky \u00faverov\u00e9ho rizika<\/h2>\n
    \n
  • PD (Probability of Default):<\/strong> pravdepodobnos\u0165, \u017ee dl\u017en\u00edk v horizonte (napr. 12 mesiacov) nespln\u00ed z\u00e1v\u00e4zky.<\/li>\n
  • LGD (Loss Given Default):<\/strong> o\u010dak\u00e1van\u00e1 strata pri defaulte po zoh\u013eadnen\u00ed zabezpe\u010denia a vymo\u017eite\u013enosti.<\/li>\n
  • EAD (Exposure at Default):<\/strong> expoz\u00edcia v okamihu defaultu (istina + akruovan\u00e9 \u00faroky, \u010derpan\u00e1 \u010das\u0165 r\u00e1mca).<\/li>\n
  • ECL (Expected Credit Loss):<\/strong> o\u010dak\u00e1van\u00e1 kreditn\u00e1 strata = PD \u00d7 LGD \u00d7 EAD; pou\u017e\u00edva sa v \u00fa\u010dtovnom v\u00fdkazn\u00edctve a oce\u0148ovan\u00ed rizika.<\/li>\n<\/ul>\n

    Vstupy hodnotenia bonity: d\u00e1ta a zdroje<\/h2>\n
      \n
    • Identifika\u010dn\u00e9 a demografick\u00e9 \u00fadaje:<\/strong> vek, rodinn\u00fd stav, po\u010det z\u00e1visl\u00fdch os\u00f4b, bydlisko, d\u013a\u017eka pobytu.<\/li>\n
    • Pr\u00edjmy a ich stabilita:<\/strong> zamestnanie (typ zmluvy, seniorita), podnikate\u013esk\u00fd pr\u00edjem (hist\u00f3ria, sez\u00f3nnos\u0165), in\u00e9 pr\u00edjmy.<\/li>\n
    • V\u00fddavky a z\u00e1v\u00e4zky:<\/strong> spl\u00e1tky existuj\u00facich \u00faverov, leasingov, n\u00e1jom, pravideln\u00e9 platby, \u017eivotn\u00e9 n\u00e1klady.<\/li>\n
    • \u00daverov\u00e1 hist\u00f3ria:<\/strong> platobn\u00e1 discipl\u00edna, delikvencie, po\u010det a vek \u00fa\u010dtov, vyu\u017eitie limitov, re\u0161trukturaliz\u00e1cie.<\/li>\n
    • Extern\u00e9 registre a \u00faverov\u00e9 kancel\u00e1rie:<\/strong> negat\u00edvne\/pozit\u00edvne registre, dopyty, bankrotov\u00e9 a exeku\u010dn\u00e9 z\u00e1znamy.<\/li>\n
    • \u00da\u010del a zabezpe\u010denie \u00faveru:<\/strong> LTV pri hypot\u00e9ke, kvalita kolater\u00e1lu, zmluvn\u00e9 kovenanty.<\/li>\n
    • Transak\u010dn\u00e9 d\u00e1ta:<\/strong> bankov\u00e9 v\u00fdpisy, cash-flow, spr\u00e1vanie na \u00fa\u010dte (v\u00fdkyvy, chargebacky), open banking<\/em> agreg\u00e1ty.<\/li>\n<\/ul>\n

      Ukazovatele platobnej schopnosti a zadl\u017eenosti<\/h2>\n
        \n
      • DTI (Debt-to-Income):<\/strong> DTI = mesa\u010dn\u00e9 spl\u00e1tky dlhov \/ \u010dist\u00fd mesa\u010dn\u00fd pr\u00edjem<\/code>.<\/li>\n
      • DSTI (Debt Service-to-Income):<\/strong> roz\u0161\u00edren\u00fd DTI o bud\u00facu spl\u00e1tku posudzovan\u00e9ho \u00faveru pri stresovej sadzbe.<\/li>\n
      • LTV (Loan-to-Value):<\/strong> LTV = v\u00fd\u0161ka \u00faveru \/ hodnota zabezpe\u010denia<\/code> (pri zabezpe\u010den\u00fdch \u00faveroch).<\/li>\n
      • Vyu\u017eitie limitu (utilization):<\/strong> \u010derpanie \/ disponibiln\u00fd r\u00e1mec<\/code> pri revolvingoch; vy\u0161\u0161ie vyu\u017eitie signalizuje nap\u00e4t\u00e9 cash-flow.<\/li>\n
      • Zostatkov\u00e1 likvidita:<\/strong> disponibiln\u00fd pr\u00edjem po zaplaten\u00ed v\u0161etk\u00fdch z\u00e1v\u00e4zkov a \u017eivotn\u00fdch n\u00e1kladov.<\/li>\n<\/ul>\n

        Kon\u0161trukcia kreditn\u00e9ho sk\u00f3re: met\u00f3dy a modely<\/h2>\n
          \n
        • Sk\u00f3rekarty (scorecards):<\/strong> tradi\u010dne logistick\u00e1 regresia s kategorizovan\u00fdmi premenn\u00fdmi, Weight of Evidence (WoE)<\/em> transform\u00e1ciou a Information Value (IV)<\/em> pre v\u00fdber \u010d\u0155t. V\u00fdhodou je interpretovate\u013enos\u0165.<\/li>\n
        • Strojov\u00e9 u\u010denie:<\/strong> gradient boosting, n\u00e1hodn\u00e9 lesy, neur\u00f3nov\u00e9 siete; vy\u0161\u0161ia predik\u010dn\u00e1 sila, nutnos\u0165 explainability<\/em> (SHAP, LIME) a robustnej valid\u00e1cie.<\/li>\n
        • Hybridn\u00e9 pr\u00edstupy:<\/strong> ML pre predv\u00fdber \u010d\u0155t a nelinearity, fin\u00e1lna sk\u00f3rekarta pre interpret\u00e1ciu a governance.<\/li>\n
        • Kalibr\u00e1cia:<\/strong> mapovanie sk\u00f3re na PD pomocou Platt scaling<\/em> alebo isotonic regression<\/em>, n\u00e1sledn\u00e1 rekalibr\u00e1cia na portf\u00f3liov\u00fa z\u00e1klad\u0148u.<\/li>\n<\/ul>\n

          Kvalita modelu: diskrimina\u010dn\u00e1 sila a stabilita<\/h2>\n
            \n
          • AUC\/Gini:<\/strong> plocha pod ROC (AUC) a Gini = 2\u00d7AUC \u2212 1; meraj\u00fa separa\u010dn\u00fa schopnos\u0165 medzi dobr\u00fdmi a zl\u00fdmi.<\/li>\n
          • KS \u0161tatistika:<\/strong> maxim\u00e1lny rozdiel medzi kumulat\u00edvami \u201egood\u201c a \u201ebad\u201c popul\u00e1cie.<\/li>\n
          • Population Stability Index (PSI):<\/strong> citlivos\u0165 na zmeny popul\u00e1cie v \u010dase; varovanie pred driftom<\/em>.<\/li>\n
          • Backtesting a out-of-time<\/em> valid\u00e1cia:<\/strong> testovanie na d\u00e1tach mimo tr\u00e9novacieho obdobia a segmentov.<\/li>\n<\/ul>\n

            Pr\u00edklady typick\u00fdch \u010d\u0155t vo spotrebite\u013eskom sk\u00f3re<\/h2>\n\n\n\n\n\n\n\n\n
            Premenn\u00e1<\/th>\nPopis<\/th>\nVplyv na sk\u00f3re (typicky)<\/th>\n<\/tr>\n<\/thead>\n
            Vek \u00fa\u010dtu (mesiace)<\/td>\nPriemern\u00fd \u201eage\u201c \u00faverov\u00fdch \u00fa\u010dtov<\/td>\nVy\u0161\u0161\u00ed vek \u2192 lep\u0161ia stabilita<\/td>\n<\/tr>\n
            Po\u010det ome\u0161kan\u00ed > 30 dn\u00ed (12M)<\/td>\nDelikvencie za posledn\u00fdch 12 mesiacov<\/td>\nViac ome\u0161kan\u00ed \u2192 v\u00fdrazn\u00e9 zhor\u0161enie<\/td>\n<\/tr>\n
            Utilization revolving<\/td>\nPodiel \u010derpania na limite<\/td>\nNad 80 % \u2192 rizikov\u00e9 spr\u00e1vanie<\/td>\n<\/tr>\n
            Stabilita pr\u00edjmu<\/td>\nVariabilita pr\u00edjmov\/mesiac<\/td>\nVy\u0161\u0161ia variabilita \u2192 slab\u0161ie sk\u00f3re<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

            Rozdiely medzi spotrebite\u013esk\u00fdm a firemn\u00fdm (SME\/corporate) hodnoten\u00edm<\/h2>\n
              \n
            • Spotrebite\u013e:<\/strong> zameranie na spr\u00e1vanie a register; jednoduch\u0161ie cash-flow, men\u0161ie objemy, viac automatiz\u00e1cie.<\/li>\n
            • SME:<\/strong> finan\u010dn\u00e9 v\u00fdkazy (EBITDA, pokrytie \u00farokov), sektorov\u00e9 rizik\u00e1, kvalita riadenia, dod\u00e1vate\u013esko-odberate\u013esk\u00e9 koncentr\u00e1cie.<\/li>\n
            • Corporate:<\/strong> detailn\u00e9 finan\u010dn\u00e9 modely, scen\u00e1re, kovenanty, kolater\u00e1ly, ratingov\u00e9 metodiky podobn\u00e9 agent\u00faram.<\/li>\n<\/ul>\n

              Schva\u013eovac\u00ed proces a rozhodovacia strat\u00e9gia<\/h2>\n
                \n
              1. Predspracovanie:<\/strong> verifik\u00e1cia identity (KYC), pr\u00edjmov, zamestnania a zdrojov d\u00e1t.<\/li>\n
              2. Sk\u00f3rovanie a PD odhad:<\/strong> aplik\u00e1cia sk\u00f3rekarty\/ML modelu, PD mapping.<\/li>\n
              3. Pravidl\u00e1 a v\u00fdnimky:<\/strong> cut-off sk\u00f3re, tvrd\u00e9 pravidl\u00e1 (napr. akt\u00edvna exek\u00facia), manu\u00e1lne v\u00fdnimky s od\u00f4vodnen\u00edm.<\/li>\n
              4. Cena a limit:<\/strong> risk-based pricing, stanovenie \u00faverov\u00e9ho limitu a splatnosti pod\u013ea rizika a zabezpe\u010denia.<\/li>\n
              5. Dokument\u00e1cia a podpis:<\/strong> zmluvy, kovenanty, informovanie o rozhodnut\u00ed a d\u00f4vodoch (adverse action notice).<\/li>\n<\/ol>\n

                Fairness, etika a regul\u00e1cia<\/h2>\n
                  \n
                • GDPR a minimaliz\u00e1cia d\u00e1t:<\/strong> sprac\u00favanie len nevyhnutn\u00fdch \u00fadajov, jasn\u00fd \u00fa\u010del, s\u00fahlas pri nad\u0161tandardn\u00fdch zdrojoch.<\/li>\n
                • Nediskrimin\u00e1cia:<\/strong> elimin\u00e1cia priamej a nepriamej diskrimin\u00e1cie (pohlavie, rasa, vek); testy fair lending<\/em> a bias<\/em> audit.<\/li>\n
                • Transparentnos\u0165 a vysvetlite\u013enos\u0165:<\/strong> zrozumite\u013en\u00e9 d\u00f4vody zamietnutia, vysvetlenie k\u013e\u00fa\u010dov\u00fdch faktorov skor\u00e9ho varovania aj pre ML modely (napr. SHAP top faktory).<\/li>\n<\/ul>\n

                  Makroekonomika, cykly a stresov\u00e9 testy<\/h2>\n
                    \n
                  • Point-in-time vs. Through-the-cycle PD:<\/strong> cyklick\u00e9 vs. vyhladen\u00e9 odhady; pou\u017eitie pod\u013ea \u00fa\u010delu (schva\u013eovanie vs. kapit\u00e1l).<\/li>\n
                  • Stresovanie:<\/strong> scen\u00e1re rastu sadzieb, nezamestnanosti, infl\u00e1cie a poklesu cien akt\u00edv; dopad na PD, LGD a kapit\u00e1l.<\/li>\n
                  • V\u010dasn\u00e9 varovanie:<\/strong> spr\u00e1vanie \u00fa\u010dtov (zv\u00fd\u0161en\u00e9 \u010derpania, klesaj\u00face zostatky, mikro-delikvencie), watch list<\/em>.<\/li>\n<\/ul>\n

                    Riadenie modelov (model risk management) a governance<\/h2>\n
                      \n
                    • \u017divotn\u00fd cyklus modelu:<\/strong> v\u00fdvoj \u2192 nez\u00e1visl\u00e1 valid\u00e1cia \u2192 schv\u00e1lenie \u2192 monitorovanie \u2192 rekalibr\u00e1cia \u2192 odsun.<\/li>\n
                    • Dokument\u00e1cia:<\/strong> d\u00e1tov\u00e9 zdroje, predspracovanie, v\u00fdber \u010d\u0155t, metodika, limity pou\u017eitia, zn\u00e1me slabiny.<\/li>\n
                    • Monitorovanie:<\/strong> champion\u2013challenger<\/em>, drift \u010d\u0155t, stabilita koeficientov, v\u00fdkonov\u00e9 metriky a alerty<\/em>.<\/li>\n
                    • Bezpe\u010dnos\u0165 a audit:<\/strong> z\u00e1znamy o verzi\u00e1ch, kontrola pr\u00edstupov, reproducibilita v\u00fdsledkov.<\/li>\n<\/ul>\n

                      Typick\u00e9 chyby a skreslenia pri hodnoten\u00ed bonity<\/h2>\n
                        \n
                      • Sample bias a reject inference<\/em>:<\/strong> model tr\u00e9novan\u00fd len na schv\u00e1len\u00fdch \u017eiadate\u013eoch; potreba imput\u00e1cie inform\u00e1cie o zamietnut\u00fdch.<\/li>\n
                      • Data leakage:<\/strong> pou\u017eitie inform\u00e1ci\u00ed nedostupn\u00fdch v \u010dase rozhodovania; vedie k nerealistick\u00e9mu v\u00fdkonom.<\/li>\n
                      • Overfitting:<\/strong> nadmern\u00e1 komplexnos\u0165 modelu bez generaliz\u00e1cie; kontrola cez out-of-time<\/em> testy.<\/li>\n
                      • Proxy diskrimin\u00e1cia:<\/strong> ne\u00famyseln\u00e9 vyu\u017eitie premenn\u00fdch korelovan\u00fdch s chr\u00e1nen\u00fdmi atrib\u00fatmi.<\/li>\n<\/ul>\n

                        Vyu\u017eitie sk\u00f3re v \u017eivotnom cykle klienta<\/h2>\n
                          \n
                        • Origin\u00e1cia:<\/strong> schva\u013eovanie, limitovanie, pricing.<\/li>\n
                        • Behavior\u00e1lne sk\u00f3re:<\/strong> pravideln\u00e9 prehodnocovanie hran\u00edc, proakt\u00edvne ponuky zv\u00fd\u0161enia\/zn\u00ed\u017eenia r\u00e1mca.<\/li>\n
                        • Collection sk\u00f3re:<\/strong> segment\u00e1cia dl\u017en\u00edkov v ome\u0161kan\u00ed (self-cure vs. vysok\u00e9 riziko), vo\u013eba strat\u00e9gie vym\u00e1hania.<\/li>\n
                        • Retencia a cross-sell:<\/strong> pravdepodobnos\u0165 pred\u010dasn\u00e9ho splatenia, pravdepodobnos\u0165 reakcie na ponuku.<\/li>\n<\/ul>\n

                          Pr\u00edklad rozhodovacej matice (zjednodu\u0161en\u00fd)<\/h2>\n\n\n\n\n\n\n\n\n
                          PD p\u00e1smo (ro\u010dn\u00e9)<\/th>\nRozhodnutie<\/th>\nCena\/Mar\u017ea<\/th>\nDoplnkov\u00e9 podmienky<\/th>\n<\/tr>\n<\/thead>\n
                          < 1 %<\/td>\nSchv\u00e1li\u0165<\/td>\nN\u00edzka mar\u017ea<\/td>\nMo\u017enos\u0165 vy\u0161\u0161ieho limitu<\/td>\n<\/tr>\n
                          1\u20133 %<\/td>\nSchv\u00e1li\u0165<\/td>\n\u0160tandardn\u00e1 mar\u017ea<\/td>\nOverenie pr\u00edjmov + LTV \u2264 80 %<\/td>\n<\/tr>\n
                          3\u20138 %<\/td>\nPodmiene\u010dne<\/td>\nVy\u0161\u0161ia mar\u017ea<\/td>\nNi\u017e\u0161\u00ed limit, dodato\u010dn\u00e9 zabezpe\u010denie<\/td>\n<\/tr>\n
                          > 8 %<\/td>\nZamietnu\u0165<\/td>\n– <\/td>\nAlternat\u00edvny produkt\/finan\u010dn\u00e9 poradenstvo<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

                          Metodika v\u00fdpo\u010dtu affordability (dostupnos\u0165 \u00faveru)<\/h2>\n
                            \n
                          1. Stanovi\u0165 stabiln\u00fd \u010dist\u00fd pr\u00edjem<\/strong> (o\u010disten\u00fd o volatiln\u00e9 zlo\u017eky).<\/li>\n
                          2. Vypo\u010d\u00edta\u0165 nevyhnutn\u00e9 v\u00fddavky<\/strong> (b\u00fdvanie, energie, minimum na \u017eivobytie pod\u013ea ve\u013ekosti dom\u00e1cnosti).<\/li>\n
                          3. Prida\u0165 existuj\u00face spl\u00e1tky<\/strong> a bud\u00facu spl\u00e1tku<\/strong> posudzovan\u00e9ho \u00faveru pri stresovej sadzbe.<\/li>\n
                          4. Overi\u0165 DSTI<\/strong> a minim\u00e1lnu zostatkov\u00fa likviditu (napr. \u2265 15\u201320 % pr\u00edjmu).<\/li>\n<\/ol>\n

                            Implement\u00e1cia v praxi: d\u00e1tov\u00e9 a technologick\u00e9 po\u017eiadavky<\/h2>\n
                              \n
                            • D\u00e1tov\u00fd sklad a kvalita d\u00e1t:<\/strong> jednotn\u00e1 identita klienta, deduplik\u00e1cia, verzovanie atrib\u00fatov, kontroln\u00e9 sumy.<\/li>\n
                            • API a real-time rozhodovanie:<\/strong> prepojenie s registrami, open banking<\/em> poskytovate\u013emi, intern\u00fdmi slu\u017ebami v milisekundov\u00fdch SLA.<\/li>\n
                            • MLOps a monitorovanie:<\/strong> pipeline pre tr\u00e9ning, deployment, kontinu\u00e1lne sledovanie driftu a v\u00fdkonu.<\/li>\n<\/ul>\n

                              Komunik\u00e1cia s klientom a finan\u010dn\u00e1 eduk\u00e1cia<\/h2>\n
                                \n
                              • Adverse action notice:<\/strong> jasn\u00e9 uvedenie k\u013e\u00fa\u010dov\u00fdch d\u00f4vodov zamietnutia a mo\u017enost\u00ed n\u00e1pravy (zni\u017eenie vyu\u017eitia limitov, konsolid\u00e1cia, stabiliz\u00e1cia pr\u00edjmu).<\/li>\n
                              • Finan\u010dn\u00e1 gramotnos\u0165:<\/strong> odpor\u00fa\u010dania na zlep\u0161enie sk\u00f3re (v\u010dasn\u00e9 platby, dlhodob\u00e9 udr\u017eanie \u00fa\u010dtov, n\u00edzke vyu\u017eitie revolvingov, obmedzenie simult\u00e1nnych dopytov).<\/li>\n<\/ul>\n

                                Kontroln\u00fd zoznam pre robustn\u00e9 hodnotenie bonity<\/h2>\n
                                  \n
                                • Overen\u00e9 a aktu\u00e1lne pr\u00edjmy (viac zdrojov, kr\u00ed\u017eov\u00e1 verifik\u00e1cia).<\/li>\n
                                • Kompletn\u00fd register z\u00e1v\u00e4zkov a hist\u00f3ria delikvenci\u00ed.<\/li>\n
                                • Spr\u00e1vne vypo\u010d\u00edtan\u00e9 DTI\/DSTI, stress rate<\/em> a zostatkov\u00e1 likvidita.<\/li>\n
                                • Vhodn\u00fd model PD s preuk\u00e1zanou diskrimin\u00e1ciou (AUC\/Gini) a stabilitou (PSI).<\/li>\n
                                • Transparentn\u00e9 rozhodovacie pravidl\u00e1, cut-off<\/em> a eskala\u010dn\u00fd proces v\u00fdnimiek.<\/li>\n
                                • Dokumentovan\u00e9 fairness<\/strong> testy a vysvetlite\u013enos\u0165 v\u00fdstupov pre klienta.<\/li>\n<\/ul>\n

                                  Bonita a sk\u00f3re ako pilier zodpovedn\u00e9ho \u00faverovania<\/h2>\n

                                  Kvalitn\u00e9 pos\u00fadenie bonity a spr\u00e1vne kalibrovan\u00e9 kreditn\u00e9 sk\u00f3re umo\u017e\u0148uj\u00fa verite\u013eom vyv\u00e1\u017ei\u0165 obchodn\u00e9 ciele s kontrolou rizika a klientom poskytuj\u00fa primeran\u00e9, udr\u017eate\u013en\u00e9 financovanie. K\u013e\u00fa\u010dom je spoji\u0165 relevantn\u00e9 d\u00e1ta, disciplinovan\u00fd modelov\u00fd r\u00e1mec, transparentn\u00e9 rozhodovanie a etick\u00e9 z\u00e1sady tak, aby v\u00fdsledkom boli predv\u00eddate\u013en\u00e9 straty, f\u00e9rov\u00e9 ceny a dlhodobo zdrav\u00e9 portf\u00f3lio.<\/p>\n","protected":false},"excerpt":{"rendered":"

                                  \u010co tvor\u00ed kreditn\u00e9 sk\u00f3re a ako ho zlep\u0161i\u0165. Plati\u0165 na\u010das, dr\u017ea\u0165 n\u00edzke vyu\u017eitie \u00faveru a budova\u0165 pozit\u00edvnu hist\u00f3riu sa prejav\u00ed na podmienkach \u00faveru.<\/p>\n","protected":false},"author":47,"featured_media":84397,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[652],"tags":[2645,89,2646,2647,2648,2649,2650,2651],"class_list":["post-44397","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-financie","tag-bonita-a-kreditne-skore","tag-dlh","tag-dopyty","tag-historia-platieb","tag-prijem","tag-skorovacie-modely","tag-utilization","tag-zlepsenie"],"yoast_head":"\nBonita a kreditn\u00e9 sk\u00f3re: Faktory, v\u00fdpo\u010det a vplyv - Auto\u0161koly.sk<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.autoskoly.sk\/news\/bonita-a-kreditne-skore-faktory-vypocet-a-vplyv\/\" \/>\n<meta property=\"og:locale\" content=\"sk_SK\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Bonita a kreditn\u00e9 sk\u00f3re: Faktory, v\u00fdpo\u010det a vplyv - Auto\u0161koly.sk\" \/>\n<meta property=\"og:description\" content=\"\u010co tvor\u00ed kreditn\u00e9 sk\u00f3re a ako ho zlep\u0161i\u0165. 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